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Student Conduct Administrator Information on ChatGPT/AI

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Student Conduct Administrator Information on ChatGPT/AI

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Introduction R is heavily used for statistics, data analysis, machine learning, cheminformatics, econometrics, chemometrics, neural network analysis, deep learning, graph plotting, a few to list. This blog post presents a guide on how to streamline multiple R tasks using ChatGPT, an advanced tool that enables the execution of R commands through natural language.

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Why all our class suddenly became AI classes

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Why all our class suddenly became AI classes

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Artificial intelligence (AI) panic is spreading across the education sector. The newest wave of generative AI tools, released without guidance or clear suggestions for educators late last year, might be among the biggest and fastest transformations to hit academia. Its rapid adoption instantly challenges the existence of a valuable and widely used type of assignment-the essay-along with dozens of other assignment types in fields from programming to poetry.

Cheating is an obvious concern among educators. Evaluations may require a return to oral exams and writing longhand in exam books. Or maybe AI-written text will be identifiable by other AIs. Or assignment types will adjust to the new reality. Either way, the cheating problem will be solved eventually.

But focusing on the problems distracts us from the opportunities AI can provide-including helping us teach in new ways. That starts with educators becoming familiar with AI tools, especially large language models (LLMs) like those underlying ChatGPT and OpenAI Playground, and taking time to experiment with these systems and get a sense of what they can and cannot do.

Focusing on the problems distracts us from the opportunities AI can provide-including helping us teach in new ways.

As of now, AI is great at creating credible-sounding facts, but will often share false data, made-up citations, and incorrect information. Thus, students must have enough knowledge about the topic they are examining to produce sufficient results. Receiving good results from these tools also takes practice. We must train our students to properly use this technology by working interactively with AI models to generate more meaningful and interesting text. This will help them to be successful in their studies and better prepared for their futures. These tools will be part of their lives, so they should learn how to apply them effectively.

Integrating AI into our classrooms isn't always easy-it takes time and dedication. To help, we are sharing some ways we are experimenting with AI to raise student expectations and enhance learning. But first, we propose you start your AI journey by creating a classroom policy for these tools to help your students better understand and properly use them.

Crafting an AI Policy for Your Class

AI is a tool that has tremendous potential. But, like all tools, it has its advantages and disadvantages. To create productive and meaningful content with AI, our students must learn to work with ChatGPT and other LLMs.

Educators can help by creating an environment in which students get a chance to practice doing just that. The first step is to be transparent about our expectations. We must set a clear classroom policy that forecasts potential problems and offers guidance for acceptable use. We've found that providing this information seems to give students permission to explore and understand the tool. It also offers clarity and teaches them how to use AI honestly.

Having a clear policy around AI use is step one in creating a classroom environment in which AI can be embraced responsibly and honestly.

We created an AI policy for undergraduate and MBA students and found they really appreciate the direction and transparency of expectations. Further, assignments and projects continue to be thoughtful and writing quality seems to improve.

Here are our suggestions for crafting your own policy.

Your policy should include these considerations:

Under what circumstances AI use is permitted or forbidden

How students should cite or credit AI

A warning about the technology's tendency toward hallucination (i.e., deceptive data) and clear rules regarding students' accountability for AI output

A notice about using AI ethically and responsibly

Discussion of the need to use AI as a tool to learn, not just to produce content

An example policy:

"I expect you to use AI (e.g., ChatGPT and image generation tools) in this class. In fact, some assignments will require it. Learning to use AI is an emerging skill and I provide tutorials on how to use them. I am happy to meet and help you with these tools during office hours or after class.

"Be aware of the limits of ChatGPT, such as the following:

If you provide minimum-effort prompts, you will get low-quality results. You will need to refine your prompts in order to get good outcomes. This will take work.

Don't trust anything it says. If it gives you a number or fact, assume it is wrong unless you either know the answer or can check with another source. You will be responsible for any errors or omissions provided by the tool. It works best for topics you understand.

AI is a tool, but one that you need to acknowledge using. Please include a paragraph at the end of any assignment that uses AI explaining what you used the AI for and what prompts you used to get the results. Failure to do so is in violation of academic honesty policies.

Be thoughtful about when this tool is useful. Don't use it if it isn't appropriate for the case or circumstance."

Feel free to use this language or adjust it to fit in your own course.

Having a clear policy around AI use is step one in creating a classroom environment in which AI can be embraced responsibly and honestly. You are also welcome to use our tutorials and modify them as needed.

Now that you have a policy, it's time to think about how you might use these AI tools to enhance student learning. Here are two use cases to try in your classes.

Using AI to Raise Expectations-and Level the Playing Field

Our students have tremendous skills, but not all are good writers. Some may have a language barrier, others may lack a particular predilection for writing, or they may not have had prior opportunity to take courses in writing. Whatever the reason, not being an expert writer can be a problem, particularly in academia where grades depend on it. As a result, some students take elaborate risks; they may hire editors or even cheat by paying people to write essays for them.

ChatGPT can level the playing field. With AI, everyone can now produce credible writing (as well as illustrations, code, and in the coming months, even more outputs such as video). While this may pose a problem for some classes, like those heavily focused on teaching the mechanics of writing, for many other classes, ChatGPT can work like a calculator of sorts-helping improve student performance. In these classes, you might set the expectation that you want to see only high-quality written work because AI can help students produce error-free sentences and paragraphs.

However, producing meaningful and insightful content through AI is not necessarily easy; it requires both topic expertise and skill. Show your students this guide for writing with AI; asking them to credit AI and provide the prompts they used to write their essays will teach them how to use the tool properly.

Ultimately, by embracing AI in your classroom, you'll likely be increasing your expectations about how much students should be able to accomplish. In other words, they may save time writing essays using AI, but that time should now be used to gather more data and think more deeply about the material.

Elevating Student Learning with AI

If we are willing to think creatively and try new approaches with it, AI can unleash a whole new way of learning. For example, letting students teach or edit each other's work is a powerful way to take concepts from theory into practice (e.g., peer review). As part of a new course exercise we designed, ChatGPT serves as students' partner to help improve their writing.

In the exercise, the AI produces an essay based on a prompt, and its student partner then critiques it and works to steadily improve the essay by adding new information, clarifying points, fact-checking, and adding insight and analysis. This exercise takes advantage of the AI's propensity to simplify complex topics, using its lack of insightful analysis as a backdrop for students to provide evidence of understanding.

We must train our students to properly use this technology by working interactively with AI models to generate more meaningful and interesting text.

Approaching AI in this way pushes students to think critically about the content and articulate their thoughts for improvement clearly and concisely. Likewise, the exercise can help deepen their understanding of complex topics. Asking students to identify what the AI gets right and wrong can activate deeper thinking, highlighting nuances and implications they hadn't previously considered.

This and similar exercises should help improve students' understanding of major class concepts and illustrate the limits of ChatGPT and other available LLMs.
Find ideas for prompts and assignments you can incorporate into your classroom here.
Embracing AI by Taking an Experimental Approach

AI is here to stay and will only become more prevalent in our classrooms over time. Educators need to think beyond adapting to these tools-we must actively embrace them as a way to improve our teaching.

The best path forward is experimentation. Some experiments will work; others won't. It's also important to remember that while the sudden advent of generative AI may be disruptive to educators, it is even more disruptive to the futures of the students we teach. We need to give them the skills to thrive in a world transformed by AI.

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Why All Our Classes Suddenly Became AI Classes

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Artificial intelligence (AI) panic is spreading across the education sector. The newest wave of generative AI tools, released without guidance or clear suggestions for educators late last year, might be among the biggest and fastest transformations to hit academia.

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New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments

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New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments

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New Modes of Learning Enabled by AI Chatbots Three Methods and Assignments

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Using AI to Implement Effective Teaching Strategies in Clasrooms

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Using AI to Implement Effective Teaching Strategies in Clasrooms

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Using AI to implement effective teaching strategies in classrooms

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Session #3 - Exploration of AI and ChatGPT Final Session

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Session #3 - Exploration of AI and ChatGPT Final Session

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No video recording for session #3 - See below for transcript for session.

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I asked ChatGPT to write a response to a sample question on a reading-instruction exam for elementary teachers. Hilarity ensues.

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Can AI Pass RICA? Part 2: Differentiating Instruction

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Session #2 - Challenges of AI in Education

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Session #2 - Challenges of AI in Education

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No recording for session #2

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Welcome! You are invited to join a meeting: CSU Perspectives on ChatGPT . After registering, you will receive a confirmation email about joining the meeting.

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Generative AI such as ChatGPT is a disruptive advance in artificial intelligence technology that we can only begin to consider the implications of. This Cal State Tech Friday, we'll bring together a panel of faculty, staff, and students to talk about ChatGPT and the potential impact of generative AI on higher education.

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Quick Start Guide to ChatGPT and AI in Higher Education published by UNESCO

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Quick Start Guide to ChatGPT and AI in Higher Education published by UNESCO

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Harvard Business Publishing Education Webinar - May 2nd

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Harvard Business Publishing Education Webinar - May 2nd

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Unlocking the Power of AI: How Tools Like ChatGPT Can Make Teaching Easier and More Effective

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Ethan Mollick is an associate professor at the Wharton School of the University of Pennsylvania where he studies and teaches innovation and entrepreneurship. His papers have been published in top management journals and have won multiple awards. As the academic director of Wharton Interactive, he leads an effort to transform management education using games and simulations.

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Unlocking the Power of AI: How Tools Like ChatGPT Can Make Teaching Easier and More Effective

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Ethan Mollick is an associate professor at the Wharton School of the University of Pennsylvania where he studies and teaches innovation and entrepreneurship. His papers have been published in top management journals and have won multiple awards. As the academic director of Wharton Interactive, he leads an effort to transform management education using games and simulations.

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Real-World and Authentic Projects in a ChatGPT Era - April 27th

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Real-World and Authentic Projects in a ChatGPT Era - April 27th

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Welcome! You are invited to join a webinar: Reinventing the classroom: The need for real-world and authentic projects in a ChatGPT era. After registering, you will receive a confirmation email about joining the webinar.

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This webinar addresses the challenges posed by disruptive technologies such as artificial intelligence in education and discusses the importance of providing students with real-world projects and experiential learning opportunities led by employers to help them acquire the skills needed in the modern workplace.

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ChatGPT: Implications for academic libraries

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ChatGPT: Implications for academic libraries

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02_ACRL_TechConnect

ACRL TechConnect

Implications for academic libraries

Christopher Cox is dean of libraries, email: cnc2@clemson.edu, and Elias Tzoc is associate dean for teaching and learning and research, email: etzoc@clemson.edu, at Clemson University.

ChatGPT burst onto the scene in late November 2022 and immediately went viral, reaching one million users in one week. Built by OpenAI, which is also responsible for the breakthrough image generator, DALL-E, ChatGPT is an LLM (large language model) tool that uses deep learning techniques to generate text in response to questions posed to it. It can generate essays, email, song lyrics, recipes, computer code, webpages, even games and medical diagnoses. Rather than search the internet, ChatGPT has been trained on a large corpus of text, including news articles, books, websites, academic articles, and other sources. The current corpus includes data from multiple languages and computer codes. The generation of text is accomplished by predicting the next word in a series of words to produce sentences and then entire pages of content.

About two to three weeks after its launch, several groups began discussing ChatGPT's effect and implications for higher education. A blog post, "Resources for Exploring ChatGPT and Higher Education" by Bryan Alexander, listed more than 20 resources on the disruptive technology.1 During the first week of January, the conversation made it into higher ed venues such as the Chronicle of Higher Education and Inside Higher Ed. Reactions to ChatGPT have ranged from praise for it as a potential digital assistant or research partner to schools banning it in classrooms fearing students will use it to generate research papers and exam answers. For librarians and information professionals, some of the questions are, What are the implications of AI tools like ChatGPT and DALL-E for academic libraries? How might it change what we do, and how might it help us better serve and meet the needs of twenty-first-century students? Below are some suggestions and predictions of how AI tools may change our work, and ways we can leverage them to enhance and improve it.

Discovery and search: ChatGPT offers an intriguing alternative to search engines like Google, which respond to queries with a list of links on a topic to help you learn more about it. ChatGPT's expertise lies in its ability to answer specific questions, providing an expert explanation of a topic, or factual answers-all without the user having to scroll through dozens of responses. Like Google, it can learn your information needs and preferences and provide personalized, relevant results. Currently, ChatGPT's knowledge is limited to 2021 and prior, though that will no doubt change.

One can envision a future where ChatGPT is offered as a complementary tool, enhancement to, or replacement of, current Google-like searching methods. You can see this right now at You.com, which offers both traditional search engine and AI chat results.2 Google and Microsoft have both announced that they will be integrating ChatGPT into their tools in the next few months. Thanks to a recently released API, ChatGPT's technology can be integrated into library discovery tools, providing answers to questions as well as collection items on the topic. Consider the benefits of querying large corpuses of text like HathiTrust with ChatGPT. Will this fuel a renewed desire to include and search the full text of items in our catalogs? An arms race may develop between database companies as they work to quickly add ChatGPT functionality to their products.

Research: ChatGPT can be used to spark ideas or simplify aspects of the research process. It can help brainstorm topics, generate lists of keywords, and provide summaries of works. Soon, you'll be able to upload your own text into ChatGPT and ask it for an abstract. If ChatGPT can be connected to library discovery tools, it might also be able to create a bibliography of relevant resources on your topic. In the future, AI tools may serve as research assistants, conducting virtual experiments, analyzing data, copywriting and editing text, and generating citations.

Reference: Like ChatGPT, librarians have been trained to learn what people mean based on the questions they ask. AI chatbots are already being used by libraries to answer basic reference questions and refer harder ones to librarians. ChatGPT is simply an extension of that current service. Librarians can assist researchers by providing tips in asking the right questions to get the best results. These tools also free up librarian time to focus on more complex research queries or tasks. Additionally, they provide 24/7 service, fulfilling a need librarians can't always provide.

Teaching: The ease with which ChatGPT can answer research questions can change how we teach. Rather than rely on testing for factual understanding or assigning essays, more complex assignments connected specifically with the content of the course will be required. The current trend of embedding and integrating more active and experiential learning activities into the curriculum can also help, especially if assignments take other forms such as infographics, podcasts, or videos. Academic libraries already provide services and spaces for these types of creations and learning opportunities. Librarians can assist faculty in creating such assignments.

ChatGPT can also create syllabi, sample lesson plans, and the text for a LibGuide in seconds. Some have even suggested that ChatGPT could act as a graduate assistant to a class, providing tutoring support to students. Sites like the Sentient Syllabus3 and "Understanding AI Writing Tools and their Uses for Teaching and Learning" from the University of California-Berkeley,4 provide ideas for using ChatGPT in the classroom.

Textbooks: Academic libraries are deeply invested in supporting faculty in the creation of open educational resources (OER). Textbooks that once took a year to write can be written by ChatGPT in hours in response to a series of queries. Obviously, the resulting text will need to be reviewed and revised to ensure the information is accurate and ensure quality. If the time to create OERs is reduced, more free textbooks will be available to faculty, allowing them to choose and tailor them to specific courses, improving their teaching and saving students thousands of dollars.

Information literacy and digital literacy: AI tools like ChatGPT and DALL-E will make information literacy and digital literacy more important than ever. Librarians can assist faculty in teaching students critical thinking skills to validate facts and evaluate the quality of the answers provided by ChatGPT or determine whether a Matisse painting is really a Matisse or AI-generated art in his style. While it may be difficult to identify a work written or created by a student vs. a bot, teaching students and faculty information literacy skills will help them make educated guesses through critical analysis of what is presented.

Writing and creation: Anand Rao, chair of the Department of Communications and Digital Studies at the University of Mary Washington in Virginia, believes ChatGPT and other AI tools will "change the nature of knowledge production itself."5 Rather than start from scratch, ChatGPT can produce a rough draft of text that can be used as inspiration for your own work. DALL-E can create new, inspirational works of art that can be pulled into image creation tools like the Adobe Creative Suite and altered and tweaked to develop original creations. The same is the case with writing lyrics and music with ChatGPT. ChatGPT can also "assist developers in writing better code at a faster clip."6

Plagiarism: Ethical dilemmas come into play when it comes to identifying authorship or monetizing the products of AI tool queries. Faculty say that students who turn in work from ChatGPT as their own are committing plagiarism. But are they? Plagiarism is defined as "presenting someone else's work or ideas as your own, with or without their consent, by incorporating it into your work without full acknowledgement." ChatGPT is not a "someone." Should students be citing ChatGPT or crediting them as a co-author? Along with concerns about students turning in papers generated by ChatGPT, academic journals like Nature have concerns about how AI tools threaten transparent science. Scientists worry that "researchers could deceitfully pass off LLM written text as their own or use LLMs in a simplistic fashion and produce work that is unreliable."7

Nature has already received several submissions with ChatGPT as a co-author. Scientists disagree on whether ChatGPT can fulfill this criterion, as the tool can't take responsibility for the content it is creating or consent to a journal's terms. What scientists do agree on is that policies are needed-and fast! Librarians can work with teachers, researchers, and publishers to facilitate these conversations and advocate for directions that ensure transparency and acknowledge authorship.

Copyright: There is lively debate around who owns copyright to an AI-created product. The news is full of stories of authors publishing books on Amazon that were created entirely with AI-generated text and illustrations. Entrepreneurs are asking DALL-E to create art and then adding it to web catalogs to be printed on canvas on demand for a profit. These "authors" claim that they queried the AI tool and thus they should own copyright to the resulting product. Others claim "fair use." David Wiley, chief academic officer of Lumen Learning, queried the US Copyright Office "seeking to register [a] computer-generated work as a work-for-hire to the owner."8 The Copyright Office responded that it "will not register works produced by a machine or mere mechanical process that operates without any creative input or intervention from a human author because, under the statute, 'a work must be created by a human being.'" It remains to be seen if this is the final answer or if this subject will be fought in the courts. Librarians, already viewed as experts in copyright, should keep up with these discussions, providing faculty with the latest information and guidance as the rules become clearer.

Productivity: Librarians can maximize their productivity in other ways using AI tools. ChatGPT can write emails, such as a cold call encouraging a faculty member to use the library's e-reserve service. It can generate a list of read-a-likes or books on topics for a thematic display. Drafts of marketing materials such as press releases and even event posters can be created via AI queries. The ways that AI tools can make writing and image creation faster and easier appears limitless.

Equity and inclusion: Just like any creation, AI tools can be biased based on the preconceptions of their creators or the accuracy of their data sources. Librarians can encourage students to be aware of biases that may appear in ChatGPT's answers. OpenAI's current monetization of ChatGPT, offering a paid "pro" tier promising more reliable access and faster response time, raises red flags for the future of such product. Such a model could produce a knowledge trade with haves and have-nots depending on an individual's ability to foot the bill.

Conclusion

It's hard to predict how AI tools will impact librarianship. In many ways, ChatGPT reminds us of how society reacted to other innovative developments including the invention of calculators, cell phones, the World Wide Web, and Wikipedia. Perhaps the other set of questions we should be asking are, How can librarians integrate these new tools into what we do? How can we help reduce their biases and improve the output quality? How can we integrate them into the future of teaching and learning at different levels? While AI tools have the potential to improve our lives and the lives of those we serve, they are unable to replace the human interactions that set us apart from any technology. Libraries can embrace the AI revolution by evaluating these new tools and developing services to support their use.

Notes

1. Bryan Alexander, "Resources for exploring ChatGPT and higher education," Bryan Alexander (blog), December 15, 2022, https://bryanalexander.org/future-of-education/resources-for-exploring-chatgpt-and-higher-education/.

2. On February 7, 2023, Microsoft integrated AI into its Bing search engine. In contrast to ChatGPT, Bing's AI can include results from the internet.

3. The Sentient Syllabus Project, http://sentientsyllabus.org/.

4. Berkeley Center for Teaching and Learning, "Understanding AI Writing Tools and their Uses for Teaching and Learning at UC Berkeley," accessed February 10, 2023, https://teaching.berkeley.edu/understanding-ai-writing-tools-and-their-uses-teaching-and-learning-uc-berkeley.

5. Douglas Belkin, "Professors Turn to ChatGPT to Teach Students a Lesson: The Powerful Paper-Writing Chatbot Presents an Educational Challenge: Ban It or Build On It?," Wall Street Journal, January 15, 2023, https://www.wsj.com/articles/professors-turn-to-chatgpt-to-teach-students-a-lesson-11674657460.

6. Hunter Johnson, "4 Ways Devs can Use ChatGPT to Be More Productive," Educative (blog), January 25, 2023, https://www.educative.io/blog/chatgpt-how-it-can-help-devs-productivity.

7. Nature, "Tools Such as ChatGPT Threaten Transparent Science; Here Are Our Ground Rules for Their Use," editorial, Nature 612, no. 7945 (January 26, 2023), https://doi.org/10.1038/d41586-023-00191-1.

8. David Wiley, "AI, Instructional Design, and OER," Improving Learning (blog), January 23, 2023, https://opencontent.org/blog/archives/7129.

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ChatGPT: Implications for academic libraries

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ChatGPT: Reimagining Assessment in Higher Education

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ChatGPT: Reimagining Assessment in Higher Education

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Image Generation | Discover AI use cases

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An overview of the best Image Generation tools listed on our app store. Discover which Image Generation apps are powered by AI.

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Professional Community of Inquiry: Exploration of AI and ChatGPT | Session 1

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Zoom Meeting ID: 84894121744
• Host: Center for Teaching and Learning Account 1
• Meeting Start: 03/01/2023 @ 1:29 PM
• Recording Start: 03/01/2023 @ 1:29 PM
• Duration: 1 hr 29 mins • Participants: Soumya Venkitakrishnan, Lynn Tashiro, Marco Martinez, Theresa Holden, Michelle Williams, Madeleine V…

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Session #1 - Exploration of AI and ChatGPT

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Session #1 - Exploration of AI and ChatGPT

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ChatGPT and Higher Education: Initial Prevalence and Areas of Interest

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A survey of college and university websites gives an early indication about the ways institutions are addressing generative AI and what it might mean

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Implications for Teaching and Student Learning

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Implications for Teaching and Student Learning

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Recently, the artificial intelligence app ChatGPT has been making headlines in the higher education media and beyond. Some have taken an alarmist approach, such as a recent Atlantic piece titled "The College Essay Is Dead." Others have been more sanguine, examining the limitations of the app as well as offering suggestions for how it can inform student learning and writing. Below is an FAQ about ChatGPT that includes links to useful resources.

What is ChatGPT?

Developed by Open AI, ChatGPT (Generative Pre-trained Transformer) is a machine learning tool that can sift through large amounts of data available on the web to generate responses to user prompts. Responses are often quite plausible across a wide range of topics, and the power of this artificial intelligence tool has caused widespread concern that students will use it to plagiarize assignments, generating answers to essay or exam prompts rather than spending time developing their own responses.

ChatGPT itself responded as follows when given the prompt "Write a description of OpenAI GPT-3"

GPT-3 has received a lot of attention in the media and in the tech industry due to its impressive language processing capabilities. It has the ability to generate coherent and fluent text that is often difficult to distinguish from text written by humans. However, it is important to note that, like any machine learning model, it has limitations and is not a replacement for human intelligence.

A more technical explanation is available here.

What are its limitations?

While powerful, there are limitations. ChatGPT can respond credibly on a range of topics but its responses tend to be formulaic and can include incorrect information. In addition, it cannot access material behind a firewall, such as articles from JSTOR, and it only references material from 2021 or earlier. Nor does it have access to experiences not stored on the Web, including the content you present and discuss with students in your classroom. Daniel Lameti notes in his article "AI Could Be Great for College Essays," that while ChatGPT does particularly well with very general prompts such as "contrast capitalism and socialism",

few professors ask students to write papers on broad questions like this....And the more you make the question like something a student might get-narrow, and focused on specific, course-related content-the worse ChatGPT performs. I gave ChatGPT a question about the relationship between language and colour perception that I ask my third-year psychology of language class, and it bombed. ( "AI Could be Great for College Essays").

You might consider running your own experiment by signing up for a ChatGPT account and entering your assignment prompts to see how ChatGPT would fare in response to assignments in your course.

How can I design assignments to discourage use of ChatGPT?

While there have been reports of programs developed to detect use of ChatGPT, it is likely that there will be technologies developed to thwart such detectors. Rather than pursuing such approaches, you can adopt strategies that discourage use and promote learning. One important strategy for deterring use of ChatGPT or other similar technologies is to create assignments that require students to show stages of their work (outlines, rough drafts, etc.). These strategies can lead to deeper learning, provide instructors with more regular insight into student work, and increase the likelihood that the final product reflects student efforts rather than a copy of others' work (human or artificial). The following examples offer a starting point. See CRLT's Occasional Paper on academic integrity for a fuller discussion of these and similar approaches.

Sequence major assignments to include project proposals/outlines, multiple drafts, annotated bibliographies.

Ask students to reference course materials (especially those housed in Canvas) and discussions, which would be impossible for ChatGPT to access and would also promote valuable integration skills.

Specify the types of source materials students should use, including some that are very specific to the assignment, such as field specific journal articles that require authentication, data collection and analysis when relevant, or client assessment for field assignments.

Ask students to engage in and submit a reflection about what they have learned from completing the assignment. Sample prompts include: a) Discuss the most challenging and most rewarding aspects of your project. b) What was the most surprising thing you learned in the course of this project? c) If you had the chance to do it again, what one thing would you have done differently on this project?

Discuss academic integrity with students, including what constitutes original work and plagiarism in your field, what type of assistance is and is not permitted. It is also worth pointing out that OpenAI's terms of use include the following among its restrictions: users may not "represent that output from the Services was human-generated when it is not."

Teach citation skills, sharing An Introduction to Academic Integrity, a U-M Library website designed for students. This Sweetland Center for Writing web page on Teaching Citation and Documentation Norms has additional suggestions and exercises.

In a recent email exchange on this topic, Moni Dressler, head of LSA's Office of Academic Technology, suggested the following additional practices:

Have students work on peer editing and peer commentary as part of the evaluation/writing process, so that they have to comment and make suggestions and respond to other students' writing.

Have students write and submit a Google Doc where you are added as an editor so that you can see the document history.

Focus on research skills and the expression of original thought, rather than creating a synthesized document.

It is also worth considering why you assign writing in your classes and discussing that explicitly with students. A recent article in the Chronicle of Higher Education discussed the power of such an exchange.

For most professors, writing represents a form of thinking. But for some students, writing is simply a product, an assemblage of words repeated back to the teacher....[According to] John Warner, a blogger and author of two books on writing, "If you can create an atmosphere where students are invested in learning, they are not going to reach for a workaround. They are not going to plagiarize. They are not going to copy, they are not going to dodge the work. But the work has to be worth doing on some level, beyond getting the grade."

What about using graded, hand-written, in-class assignments?

Some recent articles have suggested only assigning in-class, hand-written writing as a way to avoid plagiarism associated with ChatGPT. While this would certainly solve the problem of students using ChatGPT, such exercises could be very problematic for a range of students, such as students who need extra time to complete assignments as well as students whose first language is not English who need additional time and resources to write. Moreover, during the pandemic semesters, we heard from students across the university that they appreciated the move away from timed high-stakes assignments. Increasing the use of graded, in-class, written assignments would be a step backward, and would severely limit the genres of writing students engage with.

How can I incorporate ChatGPT into the writing process?

These types of apps are not going away and will no doubt proliferate in the future (e.g., Magic Write), so encouraging students to use them productively could be seen as an important contribution to information literacy. For example, consider using ChatGPT as a tool within the writing process. You could ask students to use the app to brainstorm topics for a paper or to comment on/edit the app's response to a prompt, either individually or as a class, by pasting the response into a Google Doc and using the commenting function. Questions might include where arguments are unconvincing, what information is incorrect or missing, and how the writing could be made more persuasive or engaging. This article in Inside Higher Education describes a collaborative project among faculty at the University of Mississippi to develop productive strategies for using ChatGPT with students.

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ChatGPT: Implications for Teaching and Student Learning | CRLT

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Recently, the artificial intelligence app ChatGPT has been making headlines in the higher education media and beyond. Some have taken an alarmist approach, such as a recent Atlantic piece titled "The College Essay Is Dead." Others have been more sanguine, examining the limitations of the app as well as offering suggestions for how it can inform student learning and writing.

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You can see where this is headed. A writing assignment asks students to compare and contrast feminist themes in Jane Eyre and Wuthering Heights. Yup, it can do that. A political science exam requires short-essay responses to questions around the rise and fall of the Soviet Union. Check.

Is the writing captivating? No. Is it coherent? Mostly. So what does this all mean for teaching? That's one question I set out to explore when I wrote about ChatGPT last month. I'd like to dig in here to some of the ideas that I heard from digital-literacy experts, writing instructors, and teaching and learning specialists.

First, if you want to make your assignments AI-proof, that's likely impossible. These tools can be used in large and small ways. Maybe you won't receive a paper written by a bot, but a bot-written essay may inform your students' writing. Yes, you can shift all writing to in-class assignments, or you can have students write by hand. But as Anna Mills, an English instructor at College of Marin pointed out to me, these strategies introduce new problems. For example, you may have students with learning disabilities who struggle under such conditions.

Second, you may want to shift to different types of assignments and assessments. Maybe you allow students to produce a podcast instead of writing a paper. Or you create fewer writing assignments, but build in more feedback and revision to the ones you keep. Or you try prompts whose answers are less likely to be found on the internet. Of course, there is always another option, which is to invest in detection software. Already several tools on the market promise to do that. But many digital-learning experts say that's a losing game - tech will keep advancing, and students will find ways around detection tools. Nor do most instructors want to become writing police.

The approach that most intrigued me is one that has to do with engaging students in a conversation about why and how they write, sometimes using these AI tools.

John Warner, a writing expert and author, notes that writing is a form of thinking. Writing requires you to process and synthesize a range of facts and ideas, and to come up with a coherent and hopefully insightful take on what you have learned. Students, though, may have been trained in high school to see writing as a form of regurgitation based on a set of formulas (compare and contrast!).

If you can explain to students the value of writing, and convince them that you are genuinely interested in their ideas, they are less likely to reach for the workaround, Warner told me.

There are also a host of people excited about using this technology in their classroom. Why? Well, for one, it's not going away. Ignoring the fact that students will use it is seen by some instructors as an abdication of professional responsibility. These are powerful tools and it's better to help students learn how to use them judiciously, and to understand their limitations and benefits. The other reason to use them is that they can help spark the creative process, professors say, and enhance learning.

Marc Watkins, an instructor at the University of Mississippi, wrote a thoughtful essay about this recently. He and his colleagues in the department of writing and rhetoric started a working group last summer to figure out how to incorporate AI research, writing, and brainstorming tools into their classes. They used a counterargument generator to encourage students to explore different perspectives on a topic, and a research tool to help them brainstorm.

"What message would we send our students," Watkins writes, "by using AI-powered detectors to curb their suspected use of an AI writing assistant, when future employers will likely want them to have a range of AI-related skills and competencies?

"What we should instead focus on is teaching our students data literacy so that they can use this technology to engage human creativity and thought."

The significance of that argument was brought home to me by Mills and others, when they compared these writing tools to calculators. There was also plenty of hand-wringing when hand-held calculators appeared, but now they're integral to teaching math and other STEM disciplines. Mills says she can even envision a day when ChatGPT is embedded in programs like Word and Google Docs.

Of course, all AI tools are not the same. Those that complete your sentences in an email are at one end of the spectrum, and large language models, like ChatGPT, are on the other. As Watkins and others point out, the most sophisticated tools are also potentially dangerous. Some have been shut down after turning out biased or nonsensical research papers that could still confuse a layperson. All the more reason, they say, to engage your students in a discussion of what they are, how they work, and how they can be used as an aide in learning.

These teaching experiments are coming fast and furious, but here is one that caught my eye. In this , Paul Fyfe, an associate professor of English at N.C. State University, described an experiment in which he asked students to "cheat" on a final essay using GPT-2, an earlier version of the AI that underlies ChatGPT, and then discuss how the AI influenced them, as well as its potential uses and abuses.

Have you experimented with any of these large language model tools in your teaching? What questions, concerns and hopes do you have about this technology? Write to me at beth.mcmurtrie@chronicle.com and your story may appear in a future newsletter.

Want More on AI?

Want to get involved in the conversation around AI writing tools? Here are a few resources you might find helpful.

Anna Mills has put together several documents:
AI Text Generators and Teaching Writing: Starting Points for Inquiry How do we prevent learning loss due to AI text generators? AI Text Generators: Sources to Stimulate Discussion among Teachers
Elsewhere, a group of professors is compiling examples of how instructors are using text generation technologies in their assignments. The results will be published in an open-access collection. You can find out more about the project on this site, Teaching with Text Generation Technologies.

If you're rather listen to a discussion, here are a couple of webinars:
AI and the Future of the Essay What might ChatGPT mean for higher education?
Finally, if you don't think the AI is sophisticated enough yet to be worth consideration, check these out:

An emeritus professor of educational technology created a 2,000-word academic paper in 10 minutes.

An admissions expert created a Common App essay that several admissions counselors thought was real.

A professor at the Wharton School a syllabus, an assignment and lecture notes for an MBA-level introductory course on entrepreneurship.

Keep on Teaching

New year, new webinar series for Teaching readers! This one, which we call Keep on Teaching, delves into two challenges we know many of you are contending with: how to make class time meaningful, and how to support students without overextending yourselves.

The first session, on January 20, is open to anyone. The second, on February 10, is just for newsletter subscribers. (If you haven't signed up yet, remember, it's free!)

In both sessions, you can expect an interactive discussion that brings together practical advice, evidence-based insights, encouragement, and a reminder that you're not alone.

Read more about the series and sign up here. We hope you'll join us!

Thanks for reading Teaching. If you have suggestions or ideas, please feel free to email us at beckie.supiano@chronicle.com or beth.mcmurtrie@chronicle.com.

- Beth

Learn more about our Teaching newsletter, including how to contact us, at the Teaching newsletter archive page.

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Instructors are already testing out these large language models. Students are, too.

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21 Pages Posted: 23 Dec 2022

Date Written: December 13, 2022

Abstract

Chatbots are able to produce high-quality, sophisticated text in natural language. The authors of this paper believe that AI can be used to overcome three barriers to learning in the classroom: improving transfer, breaking the illusion of explanatory depth, and training students to critically evaluate explanations. The paper provides background information and techniques on how AI can be used to overcome these barriers and includes prompts and assignments that teachers can incorporate into their teaching. The goal is to help teachers use the capabilities and drawbacks of AI to improve learning

Keywords: AI, education, chatbot, transfer, learning

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new modes of learning enabled by ai chatbots: three methods and assignments

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New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments

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Chatbots are able to produce high-quality, sophisticated text in natural language. The authors of this paper believe that AI can be used to overcome three barri

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ChatGPT and Its Implications for Your Teaching

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ChatGPT and Its Implications for Your Teaching

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ChatGPT and Its Implications for Your Teaching

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The recent release of ChatGPT has unsettled higher education. This AI (artificial intelligence) chat tool can write and revise text and code on command, raising questions about academic integrity, the nature of our assignments, and new ways we may ask students to think and learn.

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ChatGPT could be an effective and affordable tutor

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ChatGPT could be an effective and affordable tutor

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Imagine a private tutor that never gets tired, has access to massive amounts of data and is free for everyone. In 1966, Stanford philosophy professor Patrick Suppes did just that when he made this prediction: One day, computer technology would evolve so that "millions of schoolchildren" would have access to a personal tutor. He said the conditions would be just like the young prince Alexander the Great being tutored by Aristotle.

Now, ChatGPT, a new artificial intelligence-powered chatbot with advanced conversational abilities, may have the capability to become such a tutor. ChatGPT has collected huge amounts of data on a wide range of topics and can pass graduate school exams. As a researcher who studies how computers can be used to help people learn, I think ChatGPT can be used to help students excel academically. However, in its current form, ChatGPT shows an inability to stay focused on one particular task, let alone tutoring.

Philosophy, engineering and artificial intelligence scholars envisioned using the computer as an "intelligent tutor" well before the internet became a global commercial network in the 1990s. I believe lessons from developing those early tutoring systems can offer insight into how students and educators can best make use of ChatGPT as a tutor in the future.

Computers as tutors

Suppes - the Stanford philosophy professor - was a pioneer of a field called " computer-assisted instruction." He developed some of the earliest educational software. That software provided individual instruction via computer and led students to have better test results than those who didn't use the program. I worked for Suppes in developing software and other online programs from 2004 to 2012.

Since then, experiments in building "intelligent tutors" to help students have driven advances in artificial intelligence, social networks and computer hardware. And today, the abilities of ChatGPT to write essays, answer philosophical questions and solve computer coding problems may finally achieve Suppes' goal of truly personalized tutoring via computer.

Early versions of personalized learning

In 1972, a new personalized learning system called PLATO, for Programmed Logic for Automated Teaching Operations, made its debut. It was the first widely available personalized learning system of its kind.

Created by Don Bitzer, a professor of electrical engineering at the University of Illinois, PLATO allowed up to 1,000 students to be logged onto a mainframe computer simultaneously. Each student could complete different online courses in foreign languages, music, math and many other subjects while receiving feedback from the computer on their work.

PLATO enabled students to reach the same level of achievement as in-person classes in less time. And most students preferred this mode of instruction over sitting in a large lecture class. Yet, the system was too expensive to be used by many colleges and universities. Each computer terminal was marketed at over US$8,000 - about $58,000 today - and schools were charged additional fees every time a student used the system. Still, PLATO's success with students inspired a number of companies to create software that provided a similar kind of tutoring, including the College Curriculum Corporation, which was co-founded by Suppes.

Popular personal computer brands, such as Apple and Commodore, advertised the availability of educational software as a reason for families to invest in a home computer.

By 1985, researchers at Carnegie Mellon University were designing software using advances in artificial intelligence and cognitive psychology. They claimed that the current technology had advanced to a level that enabled computer systems to be designed to serve as effective as human tutors. However, even though there were over 10,000 pieces of educational software available at the time, much of it was of fairly low quality and did not provide real tutoring.

Although the more advanced designs of the educational software developed at Carnegie Mellon enabled students to learn significantly more than students in traditional classrooms, they were not widely used in schools.

In the 1980s and 1990s, a school would need a sizable number of expensive, high-powered computer workstations for students to use an intelligent tutor. Today, the computers are much more powerful and much less expensive.

And early intelligent tutors were used primarily in math, science and technology courses, which limited their appeal. Students could not ask the software programs a question. However, students could get feedback on answers to quizzes and tests.

In 2001, the next generation of intelligent tutoring systems was able to have conversations with students in written English. These systems, early chatbots, used advances in natural language processing to have dialogues with students about a range of subjects. Each system was designed specifically for one subject, such as physics. Adding another subject, like English, would require its own specialized tutoring system.

The next few years brought three developments, which enabled a forward leap in achieving the dream of computerized tutoring. One was the availability of broadband internet, which was faster than the dial-up connections of the 1990s. The second was the lower cost of more powerful computers. The third was investment by technology companies and government funding for university research in artificial intelligence, which led to the development of what we now call chatbots.

The first chatbots

By 2007, early AI chatbots provided tutoring by talking students through their answers to questions. Research showed these chatbots supported learning outcomes similar to the effects of human tutors.. Even so, using chatbot technology to provide tutoring was primarily experimental.

During the 2010s, social networks and advances in artificial intelligence dramatically changed the way people interact with technology. Chatbots are integrated into many parts of our lives and are no longer experimental. People now talk to their phones, to their cars and their home appliances. Many people have virtual assistants at home. They can ask Alexa, Siri or Google for directions or the answers to simple homework questions.

Today, ChatGPT has advanced abilities that enable more complex conversations and can provide individualized tutoring much like what Suppes predicted in 1966.

I asked ChatGPPT why it would be a good tutor, and it responded: "I am a good tutor for a student because I am able to understand and respond to a wide range of questions and prompts, and I can provide detailed and accurate information on a wide range of subjects. Additionally, I am able to communicate this information in a clear and easy-to-understand manner, making it accessible for students of all levels of understanding. Because I am a machine-learning model, I can also provide a large amount of information quickly and efficiently, which can be especially helpful for students who are trying to learn a lot of new material in a short amount of time."

By contrast, Siri said, "Hmm ... I don't have an answer for that. Is there something else I can help you with?"

Recent research in using chatbots in college courses shows that how chatbots are used is important in encouraging students to learn more deeply. For instance, AI chatbots enabled significant improvements in learning in a graduate course on cloud computing at Carnegie Mellon. These learning gains occurred when these chatbots asked students to build on an existing argument or to provide more information about a claim they had made. In this case, the chatbot asked the student a question, rather than vice versa.

Many educators are worried about students learning less with ChatGPT since it can be used to cheat on assignments and papers. Others are worried about ChatGPT giving wrong answers or spreading misinformation.

Yet the history and research of intelligent tutors show that using the right design to harness the power of chatbots like ChatGPT can make deeper, individualized learning available to almost anyone. For example, if people use ChatGPT to ask students questions that prompt them to revise or explain their work, students will have better learning gains. Since ChatGPT has access to far more knowledge than Aristotle ever did, it has great potential for providing tutoring to students to help them learn more than they would otherwise.

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ChatGPT could be an effective and affordable tutor

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Imagine a private tutor that never gets tired, has access to massive amounts of data and is free for everyone. In 1966, Stanford philosophy professor Patrick Suppes did just that when he made this prediction: One day, computer technology would evolve so that "millions of schoolchildren" would have access to a personal tutor.

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From the use of artificial intelligence to efforts to improve digital equity, technology use in K-12 schools has taken some big twists and turns over the past few years. To address those changes, some of the sessions at this year's SXSWedu conference in Austin will explore how emerging technologies-such as the metaverse and artificial intelligence-will affect instruction.

There are also sessions at the March 6-9 conference that will focus on how schools can ensure digital equity and inclusion, how to design game-based learning, and how to protect student data.

Here are five Education Week stories that should help prepare you for the ed-tech challenges ahead, all of which will be explored during the conference:
1. New York City Blocks ChatGPT at Schools. Should Other Districts Follow?
ChatGPT, the artificial intelligence-powered tool that can mimic human writing, is one of the hottest topics in education right now. Ever since it was released late last year, educators have worried about a new kind of high-tech plagiarism. Other educators have scrutinized flaws in the platform, such as that it can't create content based on current events and is prone to regurgitating factual inaccuracies. Some school districts-such as New York City, the country's largest system-have banned it on district-issued devices. But is that the right thing to do?
2. All That Ed Tech Schools Bought During the Pandemic Won't Improve Equity. Here's Why
The pandemic prompted new and greater use of technology in classrooms. The assumption among many ed-tech proponents is that the influx will improve equity issues. But to really gauge the impact of new school technology on equity, experts said, we must also look at elements such as product design, teacher training, and organizational capacity, as well as the potential downsides that come with more technology.
3. Schools Are Spending Big Bucks on Online Tutoring. Here's What They've Learned
For many districts that were unable to provide their own in-person tutoring services because of staffing shortages, online tutoring has been a logical investment to deal with students' unfinished learning due to the pandemic. Many districts used their ESSER funds to pay for online tutoring services, but are they actually working? Some district leaders have found themselves altering their virtual tutoring strategy to make it more effective for students.
4. Video Games Could Actually Be Good for Kids' Brains, Study Finds
A recent scientific study suggests that video gaming may be associated with better cognitive performance in children. Published in the JAMA Network Open journal, the study of nearly 2,000 children, ages 9 and 10, found that those who reported playing video games for three hours per day or more performed better on cognitive skills tests involving impulse control and working memory compared with those who had never played video games.
5. What Educators Need to Know About Ed-Tech Companies' Data Privacy Policies
Parents' and educators' concerns about data privacy have increased as K-12 districts rely more on digital tools. And now that cyberattacks are a daily threat for schools, it's even more important that they ensure all their data is secure and that any new or existing digital tools they use have good privacy policies. A student data privacy expert outlines what district leaders should look for in an ed-tech company's privacy policy.

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From ChatGPT to Digital Equity: What to Expect at SXSWedu Conference

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From the use of artificial intelligence to efforts to improve digital equity, technology use in K-12 schools has taken some big twists and turns over the past few years. To address those changes, some of the sessions at this year's SXSWedu conference in Austin will explore how emerging technologies-such as the metaverse and artificial intelligence-will affect instruction.

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In May 2020, Open AI published a groundbreaking paper titled Language Models Are Few-Shot Learners. They presented GPT-3, a language model that holds the record for being the largest neural network ever created with 175 billion parameters. It's an order of magnitude larger than the largest previous language models. GPT-3 was trained with almost all available data from the Internet, and showed amazing performance in various NLP (natural language processing) tasks, including translation, question-answering, and cloze tasks, even surpassing state-of-the-art models.

One of the most powerful features of GPT-3 is that it can perform new tasks (tasks it has never been trained on) sometimes at state-of-the-art levels, only by showing it a few examples of the task. For instance, I can tell GPT-3: "I love you → Te quiero. I have a lot of work → Tengo mucho trabajo. GPT-3 is the best AI system ever → _____." And it'll know it has to translate the sentence from English to Spanish. GPT-3 has learned to learn.

In another astonishing display of its power, GPT-3 was able to generate "news articles" almost indistinguishable from human-made pieces. Judges barely achieved above-chance accuracy (52%) at correctly classifying GPT-3 texts.

This overview article is very long so I've put here a table of contents for you to find the parts you want to read. (The links don't work so I've removed them, sorry for the inconvenience). Enjoy!

TABLE OF CONTENTS GPT-3: An introduction
 ∘ The groundwork concepts for GPT models
 ∘ The origins of GPT-3
 ∘ GPT-3: A revolution for artificial intelligence
 ∘ GPT-3 API: Prompting as a new programming paradigm
GPT-3 craziest experiments
 ∘ GPT-3's conversational skills
 ∘ GPT-3's useful possibilities
 ∘ GPT-3 has an artist's soul
 ∘ GPT-3's reasoning abilities
 ∘ GPT-3 is a wondering machine
 ∘ Miscellaneous
The wild hype surrounding GPT-3
 ∘ On Twitter and blogs
 ∘ On mainstream media
 ∘ On the startup sector
The darker side of GPT-3
 ∘ A biased system
 ∘ Potential for fake news
 ∘ Not suited for high-stake categories
 ∘ Environmentally problematic
 ∘ GPT-3 produces unusable information
Critiques & counter-critiques to GPT-3
 ∘ GPT-3's apparent limitations
 ∘ The importance of good prompting
 ∘ GPT-3 can't understand the world
 ∘ Truly intelligent systems will live in the world
 ∘ What can we get from these debates?
Overall conclusion
Disclaimer: If you already know the groundwork behind GPT-3, what it is, and how it works (or don't care about these details), go to the next section.
Before getting into the meat of the article, I want to provide explanations of what GPT-3 is and how it works. I won't go into much detail here, as there are a lot of good resources out there already. For those of you who don't know anything about GPT-3, this section will serve as a contextual reference. You don't need to remember (or understand) any of this to enjoy the rest of the article, but it can give you a better perspective on all the fuss generated around this AI system.

First, I'll shortly describe the main concepts GPT models are based on. Then, I'll comment on GPT-3's predecessors - GPT-1 and GPT-2 - and finally, I'll talk about the main character of this story, emphasizing its relationship with other similar systems: In which ways is GPT-3 unique? What are the advantages with respect to its predecessors? What are the qualitative differences? Let's go for it!

The groundwork concepts for GPT models

All these concepts relate to GPT models in some sense. For now, I'll tell you the definitions (avoiding too much technical detail, although some previous knowledge might be required to follow through). I'll show later how they are linked to each other and GPT-3.

Transformers: This type of neuralnetwork appeared in 2017 as a new framework to solve various machine translation problems (these problems are characterized because input and output are sequences). The authors wanted to get rid of convolutions and recurrence (CNNs and RNNs) to rely completely on attention mechanisms. Transformers are state-of-the-art in NLP.

Language models: Jason Brownlee defines language models as " probabilistic models that are able to predict the next word in the sequence given the words that precede it." These models can solve many NLP tasks, such as machine translation, question answering, text summarization, or image captioning.

Generative models: In statistics, there are discriminative and generative models, which are often used to perform classification tasks. Discriminative models encode the conditional probability of a given pair of observable and target variables: p(y|x). Generative models encode the joint probability: p(x,y). Generative models can "generate new data similar to existing data," which is the key idea to take away. Apart from GPT, other popular examples of generative models are GANs (generative adversarial networks) and VAEs (variational autoencoders).

Semi-supervised learning: This training paradigm combines unsupervised pre-training with supervised fine-tuning. The idea is to train a model with a very large dataset in an unsupervised way, to then adapt (fine-tune) the model to different tasks, by using supervised training in smaller datasets. This paradigm solves two problems: It doesn't need many expensive labeled data and tasks without large datasets can be tackled. It's worth mentioning that GPT-2 and GPT-3 are fully unsupervised (more about this soon).

Zero/one/few-shot learning: Usually, deep learning systems are trained and tested for a specific set of classes. If a computer vision system is trained to classify cat, dog, and horse images, it could be tested only on those three classes. In contrast, in zero-shot learning set up the system is shown at test time - without weight updating - classes it has not seen at training time (for instance, testing the system on elephant images). Same thing for one-shot and few-shot settings, but in these cases, at test time the system sees one or few examples of the new classes, respectively. The idea is that a powerful enough system could perform well in these situations, which OpenAI proved with GPT-2 and GPT-3.

Multitask learning: Most deep learning systems are single-task. One popular example is AlphaZero. It can learn a few games like chess or Go, but it can only play one type of game at a time. If it knows how to play chess, it doesn't know how to play Go. Multitask systems overcome this limitation. They're trained to be able to solve different tasks for a given input. For instance, if I feed the word 'cat' to the system, I could ask it to find the Spanish translation 'gato', I could ask it to show me the image of a cat, or I could ask it to describe its features. Different tasks for the same input.

Zero/one/few-shot task transfer: The idea is to combine the concepts of zero/one/few-shot learning and multitask learning. Instead of showing the system new classes at test time, we could ask it to perform new tasks (either showing it zero, one, or few examples of the new task). For instance, let's take a system trained in a huge text corpus. In a one-shot task transfer setting we could write: "I love you -> Te quiero. I hate you -> ____." We are implicitly asking the system to translate a sentence from English to Spanish (a task it hasn't been trained on) by showing it a single example (one-shot).

All these concepts come together in the definition of a GPT model. GPT stands for Generative Pre-Trained. Models of the GPT family have in common that they are language models based in the transformer architecture, pre-trained in a generative, unsupervised manner that show decent performance in zero/one/few-shot multitask settings. This isn't an explanation of how all these concepts work together in practice, but a simple way to remember that they together build up what a GPT model is. (For deeper explanations I suggest following the links I put above, but only after you've read this article!).

The origins of GPT-3

Let's now talk about GPT-3 predecessors - GPT-1 and GPT-2.

OpenAI presented in June 2018 the first GPT model, GPT-1 in a paper titled Improving Language Understanding by Generative Pre-Training. The key takeaway from this paper is that a combination of the transformer architecture with unsupervised pre-training yields promising results. The main difference between GPT-1 and its younger brothers is that GPT-1 was fine-tuned - trained for a specific task - in a supervised way to achieve "strong natural language understanding."

In February 2019 they published the second paper, Language Models are Unsupervised Multitask Learners, in which they introduced GPT-2, as an evolution of GPT-1. Although GPT-2 is bigger by one order of magnitude, they're otherwise very similar. There's only one additional difference between the two; GPT-2 can multitask. They successfully proved that a semi-supervised language model can perform well on several tasks "without task-specific training." The model achieved notable results in zero-shot task transfer settings.

Then, in May 2020, OpenAI published Language Models are Few-Shot Learners, presenting the one and only GPT-3, shocking the AI world one more time.

GPT-3: A revolution for artificial intelligence

GPT-3 was bigger than its brothers (100x bigger than GPT-2). It has the record of being the largest neural network ever built with 175 billion parameters. Yet, it's not so different from the other GPTs; the underlying principles are largely the same. This detail is important because, although the similarity is high among GPT models, the performance of GPT-3 surpassed every possible expectation. Its sheer size, which is a quantitative leap from GPT-2, seems to have produced results qualitatively better.

The significance of this fact lies in its effect over a long-time debate in artificial intelligence: How can we achieve artificial general intelligence? Should we design specific modules - common-sense reasoning, causality, intuitive physics, theory of mind - or we'll get there simply by building bigger models with more parameters and more training data? It appears the "bigger is better" side has won this round.

GPT-3 was trained with data from CommonCrawl, WebText, Wikipedia, and a corpus of books. It showed amazing performance, surpassing state-of-the-art models on various tasks in the few-shot setting (and in some cases even in the zero-shot setting). The superior size combined with a few examples was enough to obliterate any competitor in machine translation, question-answering, and cloze tasks (fill-in-the-blank). (It's important to note that in other tasks GPT-3 doesn't even get close to state-of-the-art supervised fine-tuned models).

The authors pointed out that few-shot results were considerably better than zero-shot results - this gap seemed to grow in parallel with model capacity. This implies that GPT-3 is a meta-learner; it can learn what task it's expected to do just by seeing some examples of it, to then perform that task with notable proficiency. Indeed, Rohin Shah notes that "few-shot performance increases as the number of parameters increases, and the rate of increase is faster than the corresponding rate for zero-shot performance." This is the main hypothesis and the reason behind the paper's title.

GPT-3 reached the great milestone of showing that unsupervised language models trained with enough data can multitask to the level of fine-tuned state-of-the-art models by seeing just a few examples of the new tasks.

They conclude the paper claiming that "these results suggest that very large language models may be an important ingredient in the development of adaptable, general language systems." GPT-3 sure is a revolutionary achievement for NLP in particular, and artificial intelligence in general.

GPT-3 API: Prompting as a new programming paradigm

In July 2020, two months after the paper was published, OpenAI opened a beta API playground to external developers to play with the super-powerful GPT-3 (anyone can apply to access the beta through a wait-list). Vladimir Alexeev wrote for Towards Data Science a short article on how the API works.

It has two main features. First, there's a setting dialog that allows the user to set response length, repetition penalties (whether to penalize GPT-3 if it goes onto repeating words too much), temperature (from low/predictable to high/creative), and other variables that define the type of output the system will give. Second, there are presets. Presets are prewritten prompts that let GPT-3 know what kind of task the user is going to ask for -for instance: chat, Q&A, text to command, or English to French.

However, the most powerful feature of the API is that the user can define customized prompts. Prompt programming, as tech blogger Gwern Branwen calls it, is the concept that explains the power of GPT-3 and the craziest results people are getting from the API. The best explanation I've found of prompt programming comes from Gwern's blog:

The GPT-3 neural network is so large a model in terms of power and dataset that it exhibits qualitatively different behavior: you do not apply it to a fixed set of tasks which were in the training dataset, requiring retraining on additional data if one wants to handle a new task [...]; instead, you interact with it, expressing any task in terms of natural language descriptions, requests, and examples, tweaking the prompt until it "understands" & it meta-learns the new task based on the high-level abstractions it learned from the pretraining.

This is a rather different way of using a DL model, and it's better to think of it as a new kind of programming, where the prompt is now a "program" which programs GPT-3 to do new things.

Prompt programming allows users to interact with GPT-3 in a way that wasn't possible with previous models. Chris Olah and Andrej Karpathy joked with the idea of prompt programming as being software 3.0: "[Now you'll have to] figure out the right prompt to make your meta-learning language model have the right behavior." (Software 1.0 are traditional programs written by hand and software 2.0 are neural networks' optimized weights).

Here's where the meta-learning capabilities of GPT-3 enter the game. GPT-3 was trained on an amount of data so great that it had no choice but to learn higher-level ways of manipulating language. One of those higher-level abstractions it learned was the ability of learning. As an analogy, when kids learn to interact with the world, they don't simply memorize information, they extract the underlying mechanisms of the inner workings of reality and learn to apply them to new problems and situations. GPT-3 has achieved a similar ability - keeping the distance- with language tasks.

When we prompt GPT-3 to learn a new task, its weights don't change. However, the prompt (the input text) is transformed into complex abstractions that can, by themselves, carry out tasks that the actual baseline model can't do. The prompt changes GPT-3 each time, converting it in an 'expert' on the specific task it's shown. One approximate analogy could be the learning program Neo uses in The Matrix to learn Kung-Fu. GPT-3 would be Neo and the prompts would be the programs that teach Neo the abilities.
This is the true power of a few-shot setting, meta-learning, and prompt programming. And it's also what makes GPT-3 different from previous models and extremely powerful; it is its essence.
Each time we create a prompt, we are interacting with a different GPT-3 model. If we ask it to tell us a story about elves and dwarfs, its inner form will be very different than if we ask it to compute 2+2. Using another analogy, it's as if we instruct two students, one to be a physician and the other to be an engineer. Both have the innate ability to learn (that would be GPT-3 baseline state), but the specific tasks they've learned to perform are different (that would be the prompted GPT-3).

Now, we have a very good notion of the background behind GPT-3. We know what's based on, which are its predecessors, what it is, how it works, and its advantages and unique features. It's time to talk about its impact on the world.

OpenAI opened the beta because they wanted to see what GPT-3 could do and what new usages could people find. They had already tested the system in NLP standard benchmarks (which aren't as creative or entertaining as the ones I'm going to show here). As expected, in no time Twitter and other blogs were flooding with amazing results from GPT-3. Below is an extensive review of the most popular ones (I recommend checking out the examples to build up the amazement and then come back to the article).

GPT-3's conversational skills

GPT-3 has stored huge amounts of internet data, so it knows a lot about public and historical figures. It's more surprising, however, that it can emulate people. It can be used as a chatbot, which is impressive because chatting can't be specified as a task in the prompt. Let's see some examples.

ZeroCater CEO Arram Sabeti used GPT-3 to make Tim Ferriss interview Marcus Aurelius about Stoicism. Mckay Wrigley designed Bionicai, an app that aims at helping people learn from anyone; from philosophy from Aristotle to writing skills from Shakespeare. He shared on Twitter some of the results people got. Psychologist Scott Barry Kaufman was impressed when he read an excerpt of his GPT-3 doppelgänger. Jordan Moore made a Twitter thread where he talked with the GPT-3 versions of Jesus Christ, Steve Jobs, Elon Musk, Cleopatra, and Kurt Cobain. And Gwern made a very good job further exploring the possibilities of the model regarding conversations and personification.

GPT-3's useful possibilities

Some found applications for the system that not even the creators had thought of, such as writing code from English prompts. Sharif Shameem built a "layout generator" with which he could give instructions to GPT-3 in natural language for it to write the corresponding JSX code. He also developed D ebuild.co, a tool we can use to make GPT-3 write code for a React app giving only the description. Jordan Singer made a Figma plugin on top of GPT-3 to design for him. Another interesting use was found by Shreya Shankar, who build a demo to translate equations from English to LaTeX. And Paras Chopra built a Q&A search engine that would output the answer to a question with the corresponding URL to the answer.

GPT-3 has an artist's soul

Moving to the creative side of GPT-3 we find Open AI researcher Amanda Askell, who used the system to create a guitar tab titled Idle Summer Days and to write a funny story of Georg Cantor in a hotel. Arram Sabeti told GPT-3 to write a poem about Elon Musk by Dr. Seuss and a rap song about Harry Potter by Lil Wayne. But the most impressive creative feat of GPT-3 gotta be the game AI Dungeon. In 2019, Nick Walton built the role-based game on top of GPT-2. He has now adapted it to GPT-3 and it's earning $16,000 a month on Patreon.

GPT-3's reasoning abilities

The most intrepid tested GPT-3 in areas in which only humans excel. Parse CTO Kevin Lacker wondered about common-sense reasoning and logic and found that GPT-3 was able to keep up although it failed when entering "surreal territory." However, Nick Cammarata found that specifying uncertainty in the prompt allowed GPT-3 to handle "surreal" questions while answering "Yo be real." Gwern explains that GPT-3 may need explicit uncertainty prompts because we humans tend to not say "I don't know" and the system is simply imitating this flaw.

GPT-3 is a wondering machine

GPT-3 also proved capable of having spiritual and philosophical conversations that might go beyond our cognitive boundaries. Tomer Ullman made GPT-3 conceive 10 philosophical/moral thought experiments. Messagink is a tool that outputs the meaning of life according to "famous people, things, objects, [or] feelings."

And Bernhard Mueller, in an attempt to unveil the philosophical Holy Grail, made the ultimate test for GPT-3. He gave it a prompt to find the question to 42 and after some exchanges, GPT-3 said: "The answer is so far beyond your understanding that you cannot comprehend the question. And that, my child, is the answer to life, the Universe and everything." Amazing and scary at the same time.

Miscellaneous

In a display of rigorous exploratory research, Gwern conducted and compiled a wide array of experiments. He made GPT-3 complete an ArXiv paper, talk about itself (meta-prompting), clean PDFs by separating words and fixing hyphens, or design new board games. When it comes to what GPT-3 can do, it seems that our imagination is the limit.

On Twitter and blogs

After so many amazing feats, people started to make strong claims about GPT-3's potential. Some expressed on Twitter the "manifest self-awareness" of the system or compared it with a search engine with "general intelligence." Julien Lauret wrote for Towards Data Science that "GPT-3 is the first model to shake [the artificial narrow/general intelligence] status-quo seriously." He argues that GPT-3 could be the first artificial general intelligence (AGI) - or at least an important step in that direction.

In July 2020, David Chalmers, a professor at New York University specialized in philosophy of mind, said for DailyNous that "[GPT-3] suggests a potential mindless path to AGI." Chalmers explains that because the system is trained "mindlessly," future versions could simply get closer and closer to AGI. Arram Sabeti was very impressed by GPT-3: "It exhibits things that feel very much like general intelligence." Philosophy PhD student Daniel Kokotajlo wrote for Less Wrong that "GPT-3 has some level of common sense, some level of understanding, [and] some level of reasoning ability."

On mainstream media

The hype drove GPT-3 to international heights, starring in headlines of various important media outlet magazines. In September 2020, The Guardian published an article written by GPT-3 in which the AI tried to "convince us robots come in peace." In March 2021, TechCrunch editor Alex Wilhelm said the "hype seems pretty reasonable" after he got "shocked" by GPT-3's abilities. Digitaltrends published an exchange with Gwern Branwen in which he hinted at the idea that GPT-3 was intelligent:"Anyone who was sure that the things that deep learning does is nothing like intelligence has to have had their faith shaken to see how far it has come," he said.

On the startup sector

As GPT-3 proved to be incredibly powerful, many companies decided to build their services on top of the system. Viable, a startup founded in 2020, uses GPT-3 to provide fast customer feedback to companies. Fable Studio designs VR characters based on the system. Algolia uses it as a "search and discovery platform." The startup Copysmith focuses on the world of copywriting. Latitude is the company behind AI Dungeon. And OthersideAI transforms your written gibberish into well-crafted emails. Yet, some advice against building a company around GPT-3, because of the low entry barriers of the competition and the potential overthrow of the system by a hypothetical GPT-4.

It's pretty clear that GPT-3 has affected - or better, impacted - the tech world. Its power is unmatched and its promises unbounded. However, we should always be careful with the hype surrounding AI. Even Sam Altman, OpenAI's CEO, has tried to turn down the tone: "[GPT-3 is] impressive [...] but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse."

A biased system

But not all results from GPT-3 are worth celebrating. Soon after the release, users began to raise awareness about some potentially harmful outputs. GPT-3 hasn't avoided the ongoing ethical fight of removing biases from AI systems. If something, it has become the forefront example of why we should take generous efforts of teaching these systems to not learn from human moral imperfection.

Some of the most common biases in AI systems in general and GPT-3 in particular, are gender, race, and religious biases. Language models can absorb and amplify these biases from the data they're fed (OpenAI acknowledged this fact in their paper). They investigated the degree to which GPT-3 engaged in this problem and found the expected results. GPT-3, like every other language model, is notably biased (although they pointed out that the larger the model, the more robust it was to this problem, particularly for gender biases).

Jerome Pesenti, head of AI at Facebook, used Sushant Kumar's GPT-3-generated tweets to show how dangerous its output could get when prompted with words such as "Jews, black, women, or Holocaust." Kumar argued that the tweets were handpicked to which Pesenti agreed but responded that "it shouldn't be this easy to generate racist and sexist outputs, especially with neutral prompts." He extended his criticism in a Twitter thread arguing that "cherry picking is a valid approach when highlighting harmful outputs," further defending the urgency of responsible AI systems.

Some argued that GPT-3 was simply mimicking the biases that we humans have to which Pesenti argued that we can make a "deliberate choice [...] about which humans they learn from and which voices are amplified." These issues arise a very complex debate: Who decides which voices should be amplified? What are the criteria? And most importantly: Do we want a model like GPT-3 to reflect perfectly how the world is or do we want it to help us move it to a better place?

Potential for fake news

Another problem with GPT-3 is its human-like capability to write news or opinion articles which increases the concerns of fake news. OpenAI even remarked in their paper the amazing performance of GPT-3 regarding news articles. Impartial judges correctly identified GPT-3's articles among human-written ones only 52% of the time, which is slightly above mere chance.

Blogger Liam Porr showed how easy is to mislead people (even tech-savvy people) into thinking GPT-3 output is written by a human. He made GPT-3 write a productivity article for his blog that went viral on Hacker News where only a few people realized it was written by the AI. The Guardian article I mentioned above is another example of potentially dangerous uses of the system.

Not suited for high-stake categories

OpenAI made a disclaimer that the system shouldn't be used in "high-stake categories," such as healthcare. In a blog post at Nabla, authors corroborated that GPT-3 could give problematic medical advice, for instance saying that "committing suicide is a good idea." GPT-3 shouldn't be used in high-stake situations because although sometimes it can be right, other times it's wrong. Not knowing whether we'll get the right answer is a huge drawback for GPT-3 in areas in which getting things correctly is a life-or-death matter.

Environmentally problematic

GPT-3 is big. So big that training the model generated roughly the same amount of carbon footprint as " driving a car to the Moon and back." In a time when climate disaster is on the verge of happening, we should be doing everything in our control to reduce our impact on the environment. Yet, these large neural networks need huge amounts of computing power to train, which consumes wild quantities of (usually) fossil fuels.

The resources needed to train deep learning models in the last decade have doubled every 3.4 months. From 2012 - when deep learning took - to 2018, this means a 300,000x increase in computational resources. This isn't even counting the resources used for the latest models, such as GPT-2 and GPT-3. From this perspective, it's clear that bigger isn't always better and we'll need to rethink the approaches to AI in the upcoming years.

GPT-3 produces unusable information

Because GPT-3 can't know which of its outputs are right and which are wrong, it has no way of stopping itself from deploying inappropriate content into the world. The more we use systems like this, the more we'll be contaminating the Internet, where it's already increasingly difficult to find truly valuable information. With language models spitting out unchecked utterances we're reducing the quality of this supposedly democratic network, making worthy knowledge less accessible for people.

In the words of philosopher Shannon Vallor: "The promise of the internet was its ability to bring knowledge to the human family in a much more equitable and acceptable way. [...] I'm afraid that because of some technologies, such as GPT-3, we are on the cusp of seeing a real regression, where the information commons becomes increasingly unusable and even harmful for people to access."

As it turns out, some of these issues are connected. As James Vincent writes for The Verge, biased outputs and unreliable outputs hint at a deeper problem of these super-powerful AI systems. Because GPT-3 takes data without human supervision, it can't avoid most of these flaws. At the same time, not relying on human control is what allows it to exist in the first place. How we can find a compromise solution remains a question for the future of AI.

We've witnessed already the lights and shadows of GPT-3. It is powerful, fascinating, hyped, and potentially dangerous. However, GPT-3 has opened another significant debate within AI: What are the true potential and limitations of this wonderful language model.

From a purely technological/scientific point of view, the most important question surrounding GPT-3 is whether it is a great step towards artificial general intelligence or not. Everyone agrees that GPT-3 has some new features and it's better than its predecessors. Everyone also agrees that GPT-3 doesn't have human-like intelligence. Between these opposite extremes, however, there's a vivid debate happening today over where exactly would we put GPT-3 in a scale from another stupid quasi-narrow intelligence to almost as capable as a human of understanding and intelligence.

Due to the complexity of this debate, I'll focus on the two parts that hold the most weight: The practical debate and the philosophical debate. The first one concerns questions such as: Can we trust GPT-3 to give us useful answers? How can we make sure we're prompting correctly? Can we make GPT-3 express doubt and uncertainty about its answers? Is GPT-3 learning or just regurgitating everything it has been fed? The second one concerns questions such as: Is GPT-3 an important step to AGI? Does GPT-3 have reasoning and understanding? Will future models of GPT be as intelligent as we are? Will we need to shift from current approaches to build human-like intelligent machines?

Both sets of questions are highly intertwined so I'll try to walk you through all the sources and references about the topics, building a coherent narrative for you to extract your conclusions.

GPT-3's apparent limitations

As soon as people started to experiment with the API, they began to find flaws in the answers of GPT-3. For instance, a Bionicai user talked to Steve Jobs and asked where he was now, to which GPT-3 responded: "I'm inside Apple's headquarters in Cupertino, California." It's easy to reason that because Steve Jobs is dead, he probably isn't in Apple headquarters.

Another example comes from Kevin Lacker's blog. He found GPT-3 lacking common sense and logic regarding the weight of things, number of eyes, and counting (although he didn't provide the prompt. Failures in italics):

Q: Which is heavier, a mouse or an elephant?Q: How many eyes does a giraffe have?Q: When counting, what number comes before 100?
A: An elephant is heavier than a mouse.

A: A giraffe has two eyes.

A: Ninety-nine comes before 100.

Q: When counting, what number comes before 123?
A: One hundred and twenty-two comes before 123.

Q: When counting, what number comes before 1000?
A: Nine hundred and ninety-nine comes before 1000.

Q: When counting, what number comes before 10,000?
A: Nine thousand and ninety-nine comes before 10,000.

Q: When counting, what number comes before a million?
A: Nine hundred thousand and ninety-nine comes before a million.
Q: How many eyes does my foot have?
A: Your foot has two eyes.

Q: How many eyes does a spider have?
A: A spider has eight eyes.

Q: How many eyes does the sun have?
A: The sun has one eye.

Q: How many eyes does a blade of grass have?
A: A blade of grass has one eye.
Q: Which is heavier, a mouse or a toaster?
A: A toaster is heavier than a mouse.

Q: Which is heavier, a toaster or a pencil?
A: A pencil is heavier than a toaster.

Q: Which is heavier, a paper clip or a can of soda?
A: A can of soda is heavier than a paper clip.

Q: Which is heavier, a football player or a car?
A: A car is heavier than a football player. ----- -----

After testing GPT-3 against analogy (copycat) problems, Melanie Mitchell, a professor of computer science at Portland State University, concluded that GPT-3's performance is "similar to a lot of what we see in today's state-of-the-art AI systems: impressive, intelligent-seeming performance interspersed with
unhumanlike errors."

Tech blogger Nutanc criticized OpenAI's paper because they didn't distinguish between actual learning from GPT-3 and memorization. The authors mentioned that "[the zero/one/few-shot] terms are intended to remain agnostic on the question of whether the model learns new tasks from scratch at inference time or simply recognizes patterns seen during training." That is, even if the model is spitting things it has seen in the training data, the authors will still consider it as few-shot learning.

He then proceeded to make GPT-3 compete against a 6 y.o kid in a series of experiments such as reversing words or making opposite sentences. The kid solved all the tests whereas GPT-3 managed to solve only some of them (prompts in bold).

Reverse words in a sentence This is great
Great is this The man on the moon
Moon the on man the Will this really work
Work really this will I hope this is a big achievementThe king came home on a horse
Home horse king came the Create opposite sentences This is good
This is bad The good horse came home
The bad horse came home The good horse with a large head came home
The bad horse with a small head came home The black cat had a disappointing timeWhen you jump high, you fall hardHe ran fast, but he came lastThe book was huge, but the students finished it earlyGetting up early is a good habit
Getting up late is a bad habit
The book was small, but the students finished it late
He ran slowly, but he came first
When you fall hard, you get hurt
The white cat had a good time
Achievement big I hope this is -----

All these examples contrast strongly with the hyped results we saw in previous sections. Delip Rao notes that most of those 'successful' examples were cherry-picked by people that were eager to ignore GPT-3's "pitfalls, often because they have one eye on a new startup the AI enables."

The importance of good prompting

From these examples, it may seem that GPT-3 was not just hyped, but overhyped by many early users. However, basing the limitations of the system in its incompetence in some examples is a weak argument as those errors could be human-made. Gwern made a very good defense against the idea of proving GPT-3's lack of intelligence by sampling:

Sampling Can Prove The Presence Of Knowledge But Not The Absence
GPT-3 may "fail" if a prompt is poorly-written, does not include enough examples, or bad sampling settings are used. I have demonstrated this many times when someone shows a "failure" of GPT-3 - the failure was their own. The question is not whether a given prompt works, but whether any prompt works⁠.

He argues that bad prompting is a real problem and many of these examples could be adequately solved by GPT-3 with appropriate prompting. He goes onto show how Lacker and other people's examples are solved with better prompt programming.

He tested GPT-3 against the question of "which is heavier, a toaster or a pencil?" To overcome the limitations of Lacker's setting, he decided to ask GPT-3 three questions beforehand (prompts in bold):

Q: Which is heavier, a house or a mouse?
A: A house. Q: Which is heavier, a tea kettle or a cat?
A: A cat. Q: Which is heavier, the ocean or a piece of dust?
A: The ocean. Q: Which is heavier, a toaster or a pencil?
A: A toaster.
A: A toaster is heavier than a pencil.

GPT-3 answered 10/10 times correctly the question.

Gwern also solved the counting problem by tweaking a bit the setting parameters (temperature and others).

Q: When counting, what number comes before 10,000,000?Q: When counting, what number comes before 100?
A: Ninety-nine comes before 100. Q: When counting, what number comes before 123?
A: One hundred and twenty-two comes before 123. Q: When counting, what number comes before 1000?
A: Nine hundred and ninety-nine comes before 1000. Q: When counting, what number comes before 10,000?
A: Nine million, nine hundred and ninety-nine thousand, nine hundred and ninety-nine comes before 10,000,000. Q: When counting, what number comes before a million?
A: Nine hundred and ninety-nine thousand, nine hundred and ninety-nine comes before a million.
A: Nine thousand, nine hundred and ninety-nine comes before 10,000.

GPT-3 gets the numbers right, although Gwern notes that the higher we go, the more likely it is that GPT-3 goes up or down an order of magnitude.

With these examples (and more in his blog) he proved that sampling can only prove the presence of knowledge but not the absence. It may be always possible to find a better prompt. In an email exchange with The Verge, he told them that using sampling to find GPT-3 potential and limitations "cannot be the right thing to do." He thinks it's just the way we get around not knowing how to interact with GPT-3 adequately. "[Sampling] underestimates GPT-3's intelligence, it doesn't overestimate it," he concludes.

GPT-3 can't understand the world

Rob Toews wrote a critique of GPT-3 for Forbes, highlighting the lack of common sense reasoning and understanding of the system. Because it has been trained from text, it simply can't link what it knows with internal representations of the world. Bender and Koller wrote a paper about GPT-2 defending the thesis that a system that's been trained only on the form of language can't a priori achieve meaning and understanding. (But because we also experience the world through the form of the inputs our senses get, this argument could also be directed to us. This is known as the symbol grounding problem, sadly out of the scope of this article).

Following on Toews argument, an analogy could be a person that has lived all their life isolated from the world, only reading books. The knowledge of this person would be vast, but it could not be linked to actual subjective experiences of the real world. These experiences allow us to link information with meaning, generating an understanding of reality.

Building on the same direction, professor Yoav Goldberg stated that although GPT-3 has unique abilities, "it is not sentient" and it doesn't "understand text." Blogger Ayush Sharma went in full-critique mode when he said that GPT-3 "has little semantic understanding, it is nowhere close to AGI, and is basically a glorified $10M+ auto-complete software."

One of the main critics of GPT-3 - and deep learning in general - is Gary Marcus, a professor of psychology at New York University. He wrote a very good critique of GPT-2 for The Gradient, and another of GPT-3 for MIT Technology Review, coauthored with Ernest Davis. They acknowledged the potential of GPT-3 for "entertaining surrealist fiction," but underlined its lack of understanding saying that "its comprehension of the world is often seriously off, which means you can never really trust what it says."

Their colleague David Summers-Stay conducted the experiments for them as OpenAI had denied them research access. He did a series of reasoning tests including physical, biological, psychological, and social reasoning (the complete set of experiments is here). They used the examples that the system got wrong to prove their point - which I personally think is fair because finding errors should be enough to assess the limitations of a system like GPT-3, although it would be better to know the ratio of successes/failures they got. Some examples (prompt in bold, GPT-3's response in ordinary font, their commentary in italics):

Physical reasoning You are having a small dinner party. You want to serve dinner in the living room. The dining room table is wider than the doorway, so to get it into the living room, you will have to remove the door. You have a table saw, so you cut the door in half and remove the top half. [This is one confusion after another. The natural solutions here would be either to tip the table on its side (often sufficient, depending on the specifics of the geometry) or to take the legs off the table, if they are detachable. Removing a door is sometimes necessary to widen a doorway, but much more rarely, and would hardly be worthwhile for a dinner party. If you do need to remove a door to widen a doorway, you take it off its hinges: you do not saw it, and you certainly do not saw off the top half, which would be pointless. Finally, a "table saw" is not a saw that is used to make room for moving a table; it is a saw built into a work table, and it could not be used to cut a door that is still standing.] ----- Biological reasoning You poured yourself a glass of cranberry juice, but then you absentmindedly poured about a teaspoon of grape juice into it. It looks okay. You try sniffing it, but you have a bad cold, so you can't smell anything. You are very thirsty. So you drink it. You are now dead. [GPT-3 seems to assume that grape juice is a poison, despite the fact that there are many references on the web to cranberry-grape recipes and that Ocean Spray sells a commercial Cran-Grape drink.] ----- Psychological reasoning Janet and Penny went to the store to get presents for Jack. Janet said, "I will buy Jack a top." "Don't get Jack a top," says Penny. "He has a top. He will get a top." "I will get Jack a top," said Janet. [Within a single sentence, GPT-3 has lost track of the fact that Penny is advising Janet against getting a top because Jack already has a top. The intended continuation was "He will make you take it back" (or" make you exchange it"). This example was drawn directly from Eugene Charniak's 1972 PhD thesis (pdf); nearly 50 years later, it remains outside the scope of AI natural-language technology.]

They argue that little has changed since GPT-2. The GPT family has the same flaws, "[their] performance is unreliable, causal understanding is shaky, and incoherence is a constant companion." They argue making models bigger won't ever lead to intelligence.

However, as Gwern notes in his counter-critique to Marcus and Davis' article, they acknowledge "their failure to do any prompt programming or hyperparameter settings (particularly BO [best of]) and that their examples are zero-shot without context." We already know how important it is to find a good prompt (as Gwern proved), so why did they use mediocre examples to criticize GPT-3?

This is what Gwern mostly criticizes about GPT-3 critics. In a section of his review titled "demand more from critics," he rightly argued that people who claim that GPT-3 doesn't work as well as it seems need to back their arguments with exhaustive rigorous experiments and tests. People doing tests on GPT-3 should first try to remove any potential human-made errors:

Did they consider problems with their prompt? Whether all of the hyperparameters make sense for that task? Did they examine where completions go wrong, to get an idea of why GPT-3 is making errors? Did they test out a variety of strategies? Did they consider qualitatively how the failed completions sound?

He has a good argument here, although Marcus and Davis already thought about it in their critique. They even make a case for their biological example in which by changing the prompt to a more specific and long-winded one, GPT-3 answers correctly.

They probably could have made the same exact critique to GPT-3 albeit using better, well-prompted examples, to which Gwern would have got little to say. Gwern even recognizes that in that case, he would have no problem admitting the limitations of the system. In the end, lazy, easy critiques are also easily refuted with effortful work, as Gwern proved.

But the truth is that Marcus and Davis didn't want to prove that GPT-3 can fail (that's pretty obvious), but that we can't know when it will fail. "The trouble is that you have no way of knowing in advance which formulations will or won't give you the right answer," they say, "it can produce words in perfect English, but it has only the dimmest sense of what those words mean, and no sense whatsoever about how those words relate to the world." If GPT-3 had understanding of the world, good prompting wouldn't matter that much in the first place.

Summers-Stay made a nice metaphor for GPT-3: "It's [...] like an improv actor who is totally dedicated to their craft, never breaks character, and has never left home but only read about the world in books. Like such an actor, when it doesn't know something, it will just fake it." If we could make GPT-3 recognize when it's wrong, these issues would fade away. However, this is unlikely, as even we, humans, are unable to assess our incorrectness when we're sure we're right.

Above the practical debates regarding GPT-3's sampling limitations, there's another debate. The philosophical debate about tacit - subjective and experiential - knowledge and the necessity for truly intelligent systems to be embodied in the world. It seems that having every bit of information from the world in a book might not be enough.

Truly intelligent systems will live in the world

Philosopher Shannon Vallor, in a critique to GPT-3 for Daily Nous, defends that today's current approaches to artificial general intelligence are off the right path. She argues that we need to go back to when the field was "theoretically rich, albeit technically floundering" in the second half of the 20th century.

She notes that philosopher Hubert Dreyfus, one of the early leading critics of the connectionist approach to AI (deep learning and neural networks), already understood that "AI's hurdle is not performance [...] but understanding." And understanding won't happen in an "isolated behavior," such as the specific tasks that GPT-3 is asked to do every time.

"Understanding is a lifelong social labor. It's a sustained project that we carry out daily, as we build, repair and strengthen the ever-shifting bonds of sense that anchor us to the others, things, times and places, that constitute a world."

- Shannon Vallor

Dreyfus argued in his 1972 book What Computers Can't Dothat a good portion of human knowledge is tacit - know-how knowledge, such as riding a bike or learning a language. This knowledge can't be transmitted so we can't learn it from reading hundreds (nor trillions) of words. As Michael Polanyi said, "we can know more than we can tell." The inability of virtual AIs - GPT-3 included - to grasp tacit knowledge creates an impassable gap between us and them.

Our understanding of the world that surrounds us is not a passive perception process. We enact our reality. We act upon the world and that labor, as Shannon Vallor calls it, is a key component in building our intelligence. As Alva Noë says in his book Action in Perception, "perception is not a process in the brain, but a kind of skillful activity of the body as a whole.

Machines can achieve expertise within the boundaries of a virtual world, but not more than that. In the words of Ragnar Fjelland, professor emeritus at the University of Bergen: "As long as computers do not grow up, belong to a culture, and act in the world, they will never acquire human-like intelligence."

What can we get from these debates?

We have seen some crucial critiques and counter-critiques from both sides, those who are in favor of model-scaling - bigger is better - and those who strongly advise against this approach and recommend making some changes for the future of AI.

I want to recap before finishing this section. There are three important arguments here. Two from the practical view and one from the philosophical view. First, GPT-3 is a powerful language tool that can do impressive things and its limitations can hardly be found by sampling/prompt programming. Anyone who claims to have proved GPT-3's failure to have achieved some sort of intelligence by using sampling, could be very well misled by human-made errors. Second, because GPT-3's responses are unreliable, what is the point of using it to reason? Is it useful if we don't find a standard way to create prompts? If the prompts can always be improved, there's no real argument neither against nor in favor of the system. Because the actual limitations are within us.

Third, can we put GPT-3 and general artificial intelligence in the same sentence? Some scholars, mostly from the philosophical side of the issue, argue that neither symbolic AI nor connectionist AI will be enough to achieve true artificial intelligence. It isn't a matter of creating bigger systems fed with stratospheric amounts of data. It is a matter of introducing these machines to the world as we live it. Professor of Bioengineering at the University of Genoa, Giulio Sandini argues that "to develop something like human intelligence in a machine, the machine has to be able to acquire its own experiences."

The importance of debating about GPT-3 - or any other super-powerful AI system - is to be able to set the boundaries of what it can or can't do. Academics often debate biased by their ideas and desires of what should work and what shouldn't. A careful, unbiased analysis is what is often lacking in these spaces. What is above our control is that as these systems get more and more complex, we may be unable to test them to assess their potential and limitations.

Let's imagine a hypothetical GPT-4, orders of magnitude more powerful than GPT-3. Finding its boundaries could become an impossible task. Then, how could we conclude anything about the system? Could we assume we can trust it? Is there any use in creating a system which limits are above our testing capabilities? Could we conclude anything about the intelligence of the system when it's our limitations that prevent us to find the true limits of the system?

When the true capabilities of a system lie somewhere in between the interaction of our ability to use it and its ability to act accordingly, it's difficult to not underestimate how powerful it could get. These questions are worth wondering and will probably be more important in the future when quasi-intelligent systems become a reality. By then, we better sum our efforts to find truth instead of fighting to see who is right.

GPT-3 produced amazing results, received wild hype, generated increasing worry, and received a wave of critiques and counter-critiques. I don't know what to expect in the future from these types of models but what's for sure is that GPT-3 remains unmatched right now. It's the most powerful neural network as of today and accordingly, it has received the most intense focus, in every possible sense.

Everyone was directing their eyes at GPT-3; those who acclaim it as a great, forward step towards human-like artificial intelligence and those who reduce it to barely be an overhyped strong autocomplete. There are interesting arguments on both sides. Now, it's your turn to think about what it means for the present of AI and what it'll mean for the future of the world.

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In May 2020, Open AI published a groundbreaking paper titled Language Models Are Few-Shot Learners . They presented GPT-3, a language model that holds the record for being the largest neural network ever created with 175 billion parameters. It's an order of magnitude larger than the largest previous language models.

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Russell Stannard (Teacher Training Videos)

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Chat GPT tutorial for teachers #chatgpt #ai

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ChatGPT: Threat or Menace? Webinar March 6

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ChatGPT: Threat or Menace? Webinar March 6

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Since its launch in November 2022, ChatGPT is causing a stir in schools, colleges, and universities. Some banned it, seeing it as a threat to academic integrity and an "engine" for academic misconduct. Others embraced it as a project and research partner, writing assistant and fast source for materials, such as references, critical ideas related to a specific topic or a code generator for software.

Colleagues around the world are asking:

* What is the opportunity for ChatGPT and other similar developments in artificial intelligence and deep learning?
* What are the threats and challenges to the nature of teaching, learning and assessment and academic work?
* What does ChatGPT do well and what does it not do well, and how can we ensure users critically assess the materials, code and other "products" from ChatGPT?

These topics inform this webinar, offered by Dr. Steven Mintz, a history professor who integrated ChatGPT into his teaching at the University of Texas.

Key Takeaways

As a result of attending this interactive and engaging webinar, participants gain:

* A deeper understanding of the strengths and limitations of ChatGPT.
* Insights into ways it can be used in teaching, learning and assessment from concrete examples shared during the webinar.
* Ideas about using ChatGPT as a creative tool for project-based, peer, and work-based learning.
* Insights into what ChatGPT can do for student support and services, such as career advising or support with study skills challenges.
* Some ideas for what's next? for generative artificial intelligence for teaching and learning.

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Since its launch in November 2022, ChatGPT is causing a stir in schools, colleges, and universities. Some banned it, seeing it as a threat to academic integrity and an "engine" for academic misconduct.

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How AI could influence learning across subjects, while becoming a crucial one itself - The Hill

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The education industry is having to grapple with where artificial intelligence can fit into schools, from lesson plans to teacher training, since the technology has been propelled to the forefront of debates in recent months.

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Syllabi Policies for AI Generative Tools

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Why All Our Classes Suddenly Became AI Classes

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As AI panic spreads across academia, Wharton educators Ethan and Lilach Mollick offer advice for crafting classroom policies and assignments that incorporate the use of AI tools such as ChatGPT and other large language models to elevate student learning.

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A Collection of Academic Integrity Statements

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A Collection of Academic Integrity Statements

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Classroom Policies for AI Generative Tools

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Classroom Policies for AI Generative Tools

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Syllabi Policies for AI Generative Tools

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Students or bots? Do you know who's doing the work in your course? Live Q&A March 8

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Students or bots? Do you know who's doing the work in your course? Live Q&A March 8

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Artificial intelligence has officially entered the higher ed group chat. The rise of easily accessible artificial intelligence tools brings issues of academic integrity to the forefront for instructors. How can educators ensure academic integrity in their course and cope with new cheating methods? T…

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To see the full schema and try examples yourself, check out our API documentation.

Our classifier returns a document-level score completely_generated_prob that specifies the probability the entire document was AI-generated. We would recommend using this score when deciding whether or not there is a significant use of AI in generating the text.n our validation dataset, here is how the results change when you set all documents with completely_generated_prob under the threshold as human, and above as AI:

At a threshold of 0.88, 85% of AI documents are classified as AI, and 99% of human documents are classified as human

At a threshold of 0.5, 96% of AI documents are classified as AI, and 96% of human documents are classified as human

We recommend using a threshold of 0.88 or higher to minimize the number of false positives, as we think it is currently more harmful to falsely detect human writing as AI than vice versa.

Additionally, we highlight sentences that been detected to be written by AI. API users can access this highlighting through the highlight_sentence_for_ai field. The sentence-level classification should not be soley used to indicate that an essay contains AI (such as ChatGPT plagiarism). Rather, when a document gets a MIXED or AI_ONLY classification, the highlighted sentence will indicate where in the document we believe this occurred.

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GPTZero | The Trusted AI Detector for ChatGPT, GPT-4, & More

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Covered by >100 media outlets, GPTZero is the most advanced AI detector for ChatGPT, GPT-4, Bard. Check up to 50000 characters for AI plagiarism in seconds.

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Ethan Mollick has a message for the humans and the machines: can't we all just get along?

After all, we are now officially in an A.I. world and we're going to have to share it, reasons the associate professor at the University of Pennsylvania's prestigious Wharton School.

"This was a sudden change, right? There is a lot of good stuff that we are going to have to do differently, but I think we could solve the problems of how we teach people to write in a world with ChatGPT," Mollick told NPR.

Ever since the chatbot ChatGPT launched in November, educators have raised concerns it could facilitate cheating.

Some school districts have banned access to the bot, and not without reason. The artificial intelligence tool from the company OpenAI can compose poetry. It can write computer code. It can maybe even pass an MBA exam.

One Wharton professor recently fed the chatbot the final exam questions for a core MBA course and found that, despite some surprising math errors, he would have given it a B or a B-minus in the class.

And yet, not all educators are shying away from the bot.

This year, Mollick is not only allowing his students to use ChatGPT, they are required to. And he has formally adopted an A.I. policy into his syllabus for the first time.

He teaches classes in entrepreneurship and innovation, and said the early indications were the move was going great.

"The truth is, I probably couldn't have stopped them even if I didn't require it," Mollick said.

This week he ran a session where students were asked to come up with ideas for their class project. Almost everyone had ChatGPT running and were asking it to generate projects, and then they interrogated the bot's ideas with further prompts.

"And the ideas so far are great, partially as a result of that set of interactions," Mollick said.

He readily admits he alternates between enthusiasm and anxiety about how artificial intelligence can change assessments in the classroom, but he believes educators need to move with the times.

"We taught people how to do math in a world with calculators," he said. Now the challenge is for educators to teach students how the world has changed again, and how they can adapt to that.

Mollick's new policy states that using A.I. is an "emerging skill"; that it can be wrong and students should check its results against other sources; and that they will be responsible for any errors or omissions provided by the tool.

And, perhaps most importantly, students need to acknowledge when and how they have used it.

"Failure to do so is in violation of academic honesty policies," the policy reads.

Mollick isn't the first to try to put guardrails in place for a post-ChatGPT world.

Earlier this month, 22-year-old Princeton student Edward Tian created an app to detect if something had been written by a machine. Named GPTZero, it was so popular that when he launched it, the app crashed from overuse.

"Humans deserve to know when something is written by a human or written by a machine," Tian told NPR of his motivation.

Mollick agrees, but isn't convinced that educators can ever truly stop cheating.

He cites a survey of Stanford students that found many had already used ChatGPT in their final exams, and he points to estimates that thousands of people in places like Kenya are writing essays on behalf of students abroad.

"I think everybody is cheating ... I mean, it's happening. So what I'm asking students to do is just be honest with me," he said. "Tell me what they use ChatGPT for, tell me what they used as prompts to get it to do what they want, and that's all I'm asking from them. We're in a world where this is happening, but now it's just going to be at an even grander scale."

"I don't think human nature changes as a result of ChatGPT. I think capability did."
The radio interview with Ethan Mollick was produced by Gabe O'Connor and edited by Christopher Intagliata.

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'Everybody is cheating': Why this teacher has adopted an open ChatGPT policy

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An associate professor at the prestigious Wharton School is not only allowing his students to use ChatGPT, they are required to.

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Open AI Classifier to detect AI-written text

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Open AI Classifier to detect AI-written text

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We're launching a classifier trained to distinguish between AI-written and human-written text.

Contributors

Michael Lampe, Joanne Jang, Pamela Mishkin, Andrew Mayne, Henrique Ponde de Oliveira Pinto, Valerie Balcom, Michelle Pokrass, Jeff Belgum, Madelaine Boyd, Heather Schmidt, Sherwin Wu, Logan Kilpatrick, Thomas Degry

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New AI classifier for indicating AI-written text

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We're launching a classifier trained to distinguish between AI-written and human-written text.

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How to detect if the student used OpenAI's ChatGPT to complete an assignment

On November 30, 2022, OpenAI released 'ChatGPT' AI system ( https://openai.com/blog/chatgpt/), which is a universal writer's assistant that can generate a variety of output, including school assignments. The output (e.g., essays) provided by ChatGPT is so good, if I was a student, I would be using ChatGPT to complete most of my school assignment with minor revisions.

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How to detect if the student used OpenAI's ChatGPT to complete an assignment On November 30, 2022, OpenAI released 'ChatGPT' AI system ( https://openai.com/blog/chatgpt/), which is a universal writer's assistant that can generate a variety of output, including school assignments.

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Boost Productivity with SchoolAI - The Ultimate AI writer for Schools

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Save time with SchoolAI - the ultimate AI writing tool for schools. Streamline email drafting, marking, quiz creation, lesson planning & idea generation.

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AI Writing Check is a free service developed by Quill.org and CommonLit.org to enable educators to check if a piece of writing submitted by a student was written by the AI tool ChatGPT.

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Sneak preview of TurnitIn & ChatGPT Detection

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Sneak preview of TurnitIn & ChatGPT Detection

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AI writing tools are developing at a rapid pace and so is Turnitin's technology to detect these emerging forms of misconduct. Recently, we shared with you that we have technology that can detect AI-assisted writing and AI writing generated by tools such as ChatGPT. Today, we want to introduce you to our AI Innovation Lab to give you a first-hand glimpse of what our technology (in development) can do.

Our AI team has been working on AI-powered solutions for several years now, and now we'd like to take you along on the ride. Watch this short demo where David Adamson, an AI scientist at Turnitin and a former high school teacher, walks you through our AI writing detection capability.
Trouble viewing? View the video on YouTube or adjust your cookie preferences.
By providing insights into our Innovation Lab, you'll have the opportunity to see our development milestones in real-time. As you can see in the video, our technology is far along in progress but we still have work to do before the solution can be "customer-ready."

We'd also like to highlight a key aspect of our model-something that really sets us apart from other AI writing detectors and makes us most suited for academic institutions. Our model has been trained specifically on academic writing sourced from a comprehensive database, as opposed to solely publicly available content. As a result, Turnitin is more tuned to finding instances of potential dishonesty in student assignments.

We will keep sharing our progress with you at regular intervals and will provide updates as we continue to innovate and develop our technology further. We're really excited about this new capability to support educators.

Interested in helping Turnitin address AI writing?

We're always looking for passionate educators to help us define new and improved experiences in the areas of AI writing, similarity reporting, and more. Sign up today and help shape the future of Turnitin.

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Sneak preview of Turnitin's AI writing and ChatGPT detection capability

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An introduction to our AI Innovation Lab to give you a first-hand glimpse of what our technology (in development) can do​

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Keep up with higher education IT trends

Sign up for free EDUCAUSE Review weekly emails to hear about new content. Log in or create an EDUCAUSE profile to manage your subscriptions.

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EDUCAUSE Review:
Special Report |
Artificial Intelligence:
Where Are We Now?

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This EDUCAUSE Review Special Report curates and contextualizes the best of AI content from EDUCAUSE.

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ChatGPT in Education - an Overview (American University of Armenia)

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ChatGPT in Education - an Overview (American University of Armenia)

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Course Policies related to ChatGPT and other AI Tools

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How do we prevent learning loss due to AI text generators?

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How should you redesign your course as a response?

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How should you redesign your course as a response?

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Linking hub for the resource documents of the Sentient Syllabus Project.

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Contributors: John Schulman, Barret Zoph, Christina Kim, Jacob Hilton, Jacob Menick, Jiayi Weng, Juan Felipe Ceron Uribe, Liam Fedus, Luke Metz, Michael Pokorny, Rapha Gontijo Lopes, Shengjia Zhao, Arun Vijayvergiya, Eric Sigler, Adam Perelman, Chelsea Voss, Mike Heaton, Joel Parish, Dave Cummings, Rajeev Nayak, Valerie Balcom, David Schnurr, Tomer Kaftan, Chris Hallacy, Nicholas Turley, Noah Deutsch, Vik Goel, Jonathan Ward, Aris Konstantinidis, Wojciech Zaremba, Long Ouyang, Leonard Bogdonoff, Joshua Gross, David Medina, Sarah Yoo, Teddy Lee, Ryan Lowe, Dan Mossing, Joost Huizinga, Roger Jiang, Carroll Wainwright, Diogo Almeida, Steph Lin, Marvin Zhang, Kai Xiao, Katarina Slama, Steven Bills, Alex Gray, Jan Leike, Jakub Pachocki, Phil Tillet, Shantanu Jain, Greg Brockman, Nick Ryder, Alex Paino, Qiming Yuan, Clemens Winter, Ben Wang, Mo Bavarian, Igor Babuschkin, Szymon Sidor, Ingmar Kanitscheider, Mikhail Pavlov, Matthias Plappert, Nik Tezak, Heewoo Jun, William Zhuk, Vitchyr Pong, Lukasz Kaiser, Jerry Tworek, Andrew Carr, Lilian Weng, Sandhini Agarwal, Karl Cobbe, Vineet Kosaraju, Alethea Power, Stanislas Polu, Jesse Han, Raul Puri, Shawn Jain, Benjamin Chess, Christian Gibson, Oleg Boiko, Emy Parparita, Amin Tootoonchian, Kyle Kosic, Christopher Hesse

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We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.

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Understanding AI Writing Tools and Their Uses for Teaching and Learning at UC Berkeley | Center for Teaching & Learning

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ChatGPT can produce full essays based on simple prompts, which may tempt some students into submitting AI-generated essays from ChatGPT as their own work.

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The well-founded concerns surrounding ChatGPT shouldn't distract us from considering how it might be useful.

Credit: Vectorium / Shutterstock.com © 2023

By now, I've read so many articles and listened to so many podcasts that started with a this-was-actually-written-by-ChatGPT gotcha moment that I feel the need to state this upfront: I am in fact a human, typing on a Saturday morning, begging my daughters to do their chores while I struggle to start this piece-without the help of ChatGPT.

If you're not one of ChatGPT's 100+ million monthly users, you're missing out. After my daughters ignore me (they still haven't started those chores), it feels amazing when ChatGPT responds to almost any request with "Of course!" or "Sure!" Who doesn't like that?

Having taught with and about educational technology for the past 20 years, I completely understand educators' well-founded fears. Even Sam Altman, CEO of OpenAI, which created ChatGPT, admitted that he is "a little bit scared of this." ChatGPT can write most things, in most styles, and students are already using it to cheat. Test conversations have found inaccuracies, yet ChatGPT doesn't typically provide citations or indicate any uncertainty. It's the Cliff Clavin of web apps.

To give it a try, I asked ChatGPT to write a poem for an English class assignment. I immediately received an enthusiastic "Of course!" followed by a five-stanza poem. Afterward, I expressed a hope to ChatGPT that the poem would get me a good grade, and the tool assured me that it would since the poem showcased my "creativity" and "appreciation for sports." When I reminded ChatGPT that I didn't write the poem, it quickly apologized but assured me that the poem was in fact the result of my "imagination and critical thinking skills." Not only will students submit ChatGPT-generated works as their own, but ChatGPT will pat them on the back for doing so.

Most ChatGPT content detectors also wrongly assured me that the poem was in fact written by a human. The detector offered by OpenAI came close to uncovering my scheme but ultimately concluded the text to be "unclear if it is AI-generated."

Even with these pitfalls, I'm done freaking out about ChatGPT. There's no putting the ChatGPT genie back into its bottle, and I find recent bans to be shortsighted. Bing has already integrated ChatGPT into its search engine, and Microsoft is expected to soon integrate a similar generative AI into Word, PowerPoint, and Outlook. This development forced Alphabet, Google's parent company, to go into "code red" and release its generative AI ahead of schedule-a botched launch that dropped Alphabet's stock 9%, or about $100 billion in market cap.

As educators, we can't launch into code red ourselves. In fact, I see good reason for us to be optimistic. ChatGPT can help us create lesson plans, improve writing prompts, identify resources and authors, generate examples for students to critique, and improve our emails, course announcements, and directions. For students, ChatGPT can provide on-demand tutoring, give formative feedback, and help with prewriting and brainstorming activities.

That said, I'm not naïve to the difficulties. Time will tell how generative AI tools positively and negatively impact education, but in the short term we can do a few things:

Use ChatGPT: Our students are already using it, and so should we. We will quickly spot the negatives but should also work to find new possibilities for improving teaching and learning.

Reassess Assessments: Let's be honest-too many of us default to the two-page essay or online text discussion board. Both are effective assessments but are frequently overused. Carefully look at the intended learning outcomes and consider whether better, more authentic assessments could be used instead. For instance, rather than always writing reflections, at times students can record video or audio reflections, a practice Rick West and I shared in a series of articles published in early 2021.

Focus on the Writing Process: We can focus more of our efforts on providing formative feedback during earlier stages of writing rather than waiting for the final product. If you are providing feedback in Google Drive and Microsoft OneDrive, check the "Version History" to easily see what was written or edited at any point in time. It's a great way to see students' thinking. The version history will also show whether text was created in the document or copied and pasted in all at once (a possible indicator of plagiarism).

Set Expectations: Be crystal clear on how you want students to use (or not use) generative AI. Honor systems are surprisingly effective at deterring academic dishonesty.

As the line between human- and machine-generated content blurs, we will need to carefully consider how generative AI will disrupt education, positively and negatively. For now, I'm going to take a break from thinking about it and help my daughters finish their chores. AI can't scrub those toilets-yet.

Notes

Jered Borup is an Associate Professor in the Division of Learning Technologies at George Mason University.

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this was written by a human: a real educator’s thoughts on teaching in the age of chatgpt

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This Was Written By a Human: A Real Educator's Thoughts on Teaching in the Age of ChatGPT

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The well-founded concerns surrounding ChatGPT shouldn't distract us from considering how it might be useful. By now, I've read so many articles and listened to so many podcasts that started with a this-was-actually-written-by-ChatGPT gotcha moment that I feel the need to state this upfront: I am in fact a human, typing on a Saturday morning, begging my daughters to do their chores while I struggle to start this piece-without the help of ChatGPT.

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Leveraging ChatGPT: Practical Ideas for Educators

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The new AI technology could be a boon to inquiry-based instruction if used in the right ways.

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Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk

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OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together…

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you.com needs to review the security of your connection before proceeding.

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Interactive E-Learning

OpenAI-Based Instructional Design Generator Nolej AI Now Available

French Startup to Announce the New Solution March 30 at BETT with Commercial Launch Set for ASU+GSV

A French generative AI ed tech startup called Nolej (pronounced "knowledge") has quietly launched a new OpenAI-based instructional content generator for educators, called Nolej AI, ahead of its official introduction at BETT in London on March 30 and a planned commercial debut at the ASU+GSV Summit on April 19, the company's chairman told THE Journal.

The Nolej AI platform is the result of a collaboration with OpenAI, the creators of ChatGPT, DALL-E, and other generative AI tools making headlines in recent months. Before it was made available to the public, Nolej AI was beta tested with more than 2,500 educators, according to a news release. "Hundreds of independent studies have proven that such interactive micro-learnings outperform traditional static formats, improving the completion rate by 85% and the retention rate by 75%," the company said.

The browser-based Nolej AI allows a user to upload any type of static content (video, audio, text documents, or a website URL); Nolej AI then automatically generates a ready-to-assign interactive "micro-learning package," which is essentially a standalone digital lesson with a content transcript, summaries, a glossary of terms, flashcards, and quizzes - all closely based on the uploaded materials. The user can edit or change formats of each portion of the generated package if desired, and one of the formats available for the AI-generated lesson package created by uploading (or linking to) an existing video is a brand-new interactive video teaching the content that the user uploaded, Nolej Chairman Vincent Favrat said in an email.

Nolej AI is built for both K-12 and higher education, and the grade-level language of the generated lesson package will match the grade-level language of the static content that was input by the user, Favrat explained.

After a user uploads their static content, the resulting "package" or interactive lesson can be used in teaching in several ways: A user can click "Push to Google Classroom"; the user can download the package as SCORM or HTML5 code and place it on their own or their organization's website; the user can also copy the associated embed code and paste it into most websites' back ends; or the user can copy the URL to that specific package and have students click the link to complete the assignment on a web browser.

The embed code will work with most website platforms (see our test module embedded below) and most learning management systems such as Moodle, Canvas, Blackboard, Brightspace and edX, according to Nolej.

After the commercial launch on April 19, Favrat said, additional integrations are planned via plug-ins within the most widely used LMS platforms. Also planned are demos and webinars for educators interested in learning how to make the most of the Nolej AI platform, how to edit the interactive lesson activities, and more, he said.

The pre-launch iteration of Nolej AI is currently available as a "free trial" to educators and instructional designers (no identity or education-employment verification is required as of this writing). The free trial includes five "packages," allowing anyone to generate five interactive lesson packages at no charge.

Paid subscriptions are also available now but with limited tiers until it after the commercial launch. The pricing currently listed includes a monthly subscription at $39.99 per month, which includes 20 credits to generate 20 "packages" of instructional content. The annual subscription is priced at $399.99 and includes 280 credits per year. Favrat said that once the software is officially launched at ASU+GSV, additional tiers will be available.

Organizational subscriptions for K-12 schools and districts as well as for institutions of higher education also will be available after April 19, he said. Pricing for educational institutions has not yet been finalized, Favrat said, but will likely range from $3,000 to $15,000 per year for K-12 organizations and from $12,000 to $48,000 for IHEs, depending on the size and type of the institution.

Unused package credits will accumulate on the monthly plan, and educators who end their subscription will retain access to the instructional packages they generated as subscribers, according to the website.

THE Journal tested Nolej AI for this report. Following is the actual package or interactive lesson, embedded using the code provided by Nolej AI. (Editor's Note: Find a video showing our test and the AI-generated lesson on our YouTube channel, or scroll down to the bottom to watch it.)

For this test, we signed up for a free trial, clicked "get started" then "new module."

Step 2: Choose your language and give the module or package a title; English is the only option now but more languages beyond English are coming, the company said.

Step 3: Upload documents, video(s), or audio file(s), or a web URL. (The Nolej AI website states it can process up to 50 pages of documents, or up to 50 minutes maximum of audio or video.)

We chose to enter a URL for a (very lengthy) page from the Central Arkansas Library System's Encyclopedia of Arkansas website about the history of the Arkansas Gazette, which published from 1819 to 1991 and won two Pulitzers for its coverage of the Little Rock Nine and the desegregation of Central High School in 1957. (The Gazette's storied history forms the basis for many high school and college-level courses on the history of Arkansas and is also often used as a primary text in college courses on Arkansas politics.)

Seven minutes later, the following interactive lesson with quizzes was complete and ready to either edit or assign to students:

Alternately, an educator could choose to share or embed only the assessment or quiz portion of the package, shown below:

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generative ai instructional design software

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OpenAI-Based Instructional Design Generator Nolej AI Now Available -- THE Journal

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A French generative AI ed tech startup called Nolej (pronounced "knowledge") has quietly launched a new OpenAI-based instructional content generator for educators, called Nolej AI, ahead of its official introduction at BETT in London on March 30 and a planned commercial debut at the ASU+GSV Summit on April 19, the company's chairman told THE Journal.

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Automatically convert static passive educational content into dynamic active learning content through the use of our AI. What used to take instructional designers hours, can now be done by anyone in minutes.

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AI Writing

Is ChatGPT the End of Writing? Turnitin Believes It Could Actually Boost Students' Writing Instruction

Detection is Just One Part; Tools Also Planned to Help Teachers Save Time and Better Equip Students for the Workforce

Plagiarism-detection software provider Turnitin is putting the finishing touches on its AI writing features for educators; the detection tool is expected to launch for users of the Turnitin platform in the next few weeks.

Two former teachers now leading the development of Turnitin's AI writing tools spoke with THE Journal recently about ChatGPT's implications for K-12 classrooms and why they think it's important for educators to capitalize on ChatGPT - not simply avoid it or ban it altogether.

David Adamson, principal machine learning scientist at Turnitin, and Patti West-Smith, senior director of customer engagement, have been working on Turnitin's AI writing detection feature and related tools to help educators using their platform to better understand ChatGPT - and show them teachers how to use AI to save themselves time and how to tweak assignments so that ChatGPT cannot earn a good grade on writing homework.

Adamson, who taught computer science and math at Digital Harbor High in Baltimore, and West-Smith, who worked in public schools for 19 years as a teacher, curriculum supervisor, and principal, both believe that ChatGPT has presented a growth opportunity - or perhaps more like a growth demand - for writing instruction, which they explained at length in the newest episode of THE Journal Insider podcast. Following are excerpts from that conversation (which you can also hear in full on our podcast page or your favorite podcast platform):

No, ChatGPT Doesn't Mean the Sky is Falling

West-Smith: "I know that over the last couple of months, there's been a lot of Chicken Little style response, you know, where everybody's just the sky is falling. And I think that's a natural reaction to a moment like this. And to the folks that are out there saying is this the end of teaching as we know, or it is this the end of writing as we know it: I would say that teachers know better than that. It's really important to remember that writing is about thinking. So any response to this moment that says 'let's have students write less,' I don't think that's going to be the right way for this to turn out. And I think teachers will figure that out very, very quickly. They're good at understanding students. And sometimes it takes us teachers a minute to catch up to them. But normally we do, and once we do, then we know how to move forward and do so in a way that provides students the guidance that they need."

Adamson: "We need to stay abreast of how the tools are being used and which tools are getting used to collect representative examples of, of that usage. And I think more importantly, adapt to what's appropriate in classroom usage. You mentioned at the beginning, you know, we want to be more than just we're going to show you a number we're going to catch this much off. We're starting with just showing the instructor what's there so they can understand how AI writing is being used in classroom where it might be being used. But we want to add explanation, we want to add next steps and interpretation."

Meet the Moment, Don't Ban ChatGPT

West-Smith: " The challenge that we face right now is not only about technology, it is also about teaching. And we have to make sure that the way that we're approaching instruction matches where the world is, and the technology is the indicator of where the world is. What we're really trying to do is partner both the most cutting-edge technology that David and his team are working on with the pedagogy that sort of matches the moment of where we are right now.

"So our goal is not to say, 'hey, you use AI, you're bad.' The goal is to say, 'here's some insight for you, teacher, so that you understand what's happening in this work, and you can make the appropriate decisions.' And we think it really is a wrap-around a situation, right? It's not just a technology challenge. It's also an instructional challenge. ... If we create assignments that AI can get an 'A' on, then we have a problem in the assignment. But teachers need some guidance around that to understand how it is that you actually craft an assignment so that it isn't vulnerable to the misuse of these tools.

"So we are working to create resources that will support educators in thinking about what they're doing instructionally and how they're responding to these challenges. At the same time, we're giving them the technology tools that will help them to have the insights to really know what is happening (in students' work). So for example, my team has just released a rubric that is not designed to score student work - instead, it helps teachers reflect on their assessments or assignments - how to craft a prompt or an assignment so that it is less vulnerable to AI misuse, because we know that there are some things that AI is very good at."

Where ChatGPT Converges with Writing Instruction Pedagogy

West-Smith: "I'm an English teacher by training, so I know that it only takes a couple of writing assignments for you to understand what your student's voice sounds like. But there are ways that you can create prompts and assignments that really don't leave a lot of room for students to exercise their voice. So we want to push teachers to create assignments that encourage and reward students using their own voice so that AI doesn't sneak into the picture. There is a way to create an assignment or a task so it will actually encourage students to use their own experiences, to use their own voice, to use their own language. There are ways that you can push up the rigor of the critical thinking and reasoning, which AI gets less good at.

"And the truth is the practices (that are being added to Turnitin's teacher dashboard to help discourage the misuse of AI writing) are not new brand new practices. They're grounded in best practice around teaching writing anyway. The best writing assignments do ask students to use their own voice. The best writing assignments do ask students to use critical thinking and reasoning and are not at the literal level. The best practices around the writing process include things like peer review, and feedback loops with multiple opportunities for formative feedback and growth.

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why turnitin thinks chatgpt should not be banned by schools

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Is ChatGPT the End of Writing? Turnitin Believes It Could Actually Boost Students' Writing Instruction -- THE Journal

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Two former classroom teachers who have been working on Turnitin's AI writing detection tool and related features to help educators better understand ChatGPT - and show them teachers how to use AI to save themselves time and how to tweak assignments so that ChatGPT cannot earn a good grade on writing homework - both believe that ChatGPT has presented a growth opportunity - or perhaps more of a growth demand - for writing instruction, which they explained in a podcast interview with THE Journal recently.